<｜begin▁of▁sentence｜>### Instruction:
As a Quality Assurance (QA) Analyst in a technology company, your role involves ensuring that software systems meet the necessary quality standards and specifications. Suppose you're tasked with testing a network analysis tool that utilizes the `CirculatedNeighborsRandomWalkSampler` API in Python's NetworkX library.

Imagine the scenario: You're working on a project that involves analyzing the structure of a network generated using the Erdos-Renyi model with parameters `n=100` and `p=0.35`. Your team wants to assess the degree assortativity of a subgraph sampled from this network. Specifically, they're interested in obtaining a subgraph with 8 nodes using the `CirculatedNeighborsRandomWalkSampler` to simulate a random walk-based sampling strategy.

Your task is to implement this sampling process and then calculate the degree assortativity of the sampled subgraph. This analysis will help evaluate the network's structural properties and its potential implications for various applications, such as social network analysis or infrastructure modeling. Remember to document your testing process, results, and any recommendations for improving the sampling methodology or network analysis tool.
### Response:
import networkx as nx
from littleballoffur import CirculatedNeighborsRandomWalkSampler

G = nx.gnp_random_graph(n=100, p=0.35)

sampler = CirculatedNeighborsRandomWalkSampler(8)

sampler_G = sampler.sample(G)

result = nx.degree_assortativity_coefficient(sampler_G)

print(result)
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