human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "friendship networks and social status"
  Abstract: In empirical studies of friendship networks, participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower ranked individual claiming friendship with a higher ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.

Connected through hyperedge HE-1 (induced by source paper "measuring directed triadic closure with closure coefficients"):
  with members:
    - "the switch markov chain for sampling irregular graphs"
    - "directed closure measures for networks with reciprocity"
    - "community structure and scale free collections of erdős renyi graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "different patterns of social closeness observed in mobile phone communication"):
  with members:
    - "measuring large scale social networks with high resolution"

Connected through hyperedge HE-3 (induced by source paper "estimating network structure from unreliable measurements"):
  with members:
    - "network structure from rich but noisy data"
    - "reconstructing networks with unknown and heterogeneous errors"
    - "structural inference for uncertain networks"
    - "network structure inference a survey motivations methods and applications"
    - "measuring large scale social networks with high resolution"
    - "coauthorship and citation patterns in the physical review"
    - "a heuristic approach to author name disambiguation in bibliometrics databases for large scale research assessments"

Connected through hyperedge HE-4 (induced by source paper "assembly in populations of social networks"):
  with members:
    - "emergence of complex institutions in a large population of self governing communities"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "assembling thefacebook using heterogeneity to understand online social network assembly"
    - "laboratories of oligarchy how the iron law extends to peer production"

Connected through hyperedge HE-5 (induced by source paper "temporal stability in human interaction networks"):
  with members:
    - "versinus a visualization method for graphs in evolution"
    - "a connective differentiation of textual production in interaction networks"
    - "community detection and graph partitioning"

Connected through hyperedge HE-6 (induced by source paper "structure and inference in annotated networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "efficiently inferring community structure in bipartite networks"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "community structure detection in complex networks with partial background information"
    - "community detection in networks with node attributes"
    - "social structure of facebook networks"
    - "active learning for node classification in assortative and disassortative networks"

Connected through hyperedge HE-7 (induced by source paper "predicting risky behavior in social communities"):
  with members:
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "community detection in networks with node attributes"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "assembling thefacebook using heterogeneity to understand online social network assembly"):
  with members:
    - "network weirdness exploring the origins of network paradoxes"
    - "social resilience in online communities the autopsy of friendster"
    - "potential networks contagious communities and understanding social network structure"
    - "social structure of facebook networks"
    - "the anatomy of the facebook social graph"
    - "four degrees of separation"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.SI(Social and Information Networks)