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: "size matters a comparative analysis of community detection algorithms"
  Abstract: Understanding community structure of social media is critical due to its broad applications such as friend recommendations, user modeling and content personalizations. Existing research uses structural metrics such as modularity and conductance and functional metrics such as ground truth to measure the qualify of the communities discovered by various community detection algorithms, while overlooking a natural and important dimension, community size. Recently, anthropologist Dunbar suggests that the size of stable community in social media should be limited to 150, referred to as Dunbar's number. In this study, we propose a systematic way of algorithm comparison by orthogonally integrating community size as a new dimension into existing structural metrics for consistently and holistically evaluating the community quality in social media context. we design a heuristic clique based algorithm which controls the size and overlap of communities with adjustable parameters and evaluate it along with five state-of-the-art community detection algorithms on both Twitter network and DBLP network. Specifically, we divide the discovered communities based on their size into four classes called close friend, casual friend, acquaintance, and just a face, and then calculate the coverage, modularity, triangle participation ratio, conductance, transitivity, and the internal density of communities in each class. We discover that communities in different classes exhibit diverse structural qualities and many existing community detection algorithms tend to output extremely large communities.

Connected through hyperedge HE-1 (induced by source paper "a statistical density based analysis of graph clustering algorithm performance"):
  with members:
    - "using synthetic networks for parameter tuning in community detection"
    - "graph sketching based massive data clustering"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "extension of modularity density for overlapping community structure"
    - "axioms for graph clustering quality functions"
    - "defining and evaluating network communities based on ground truth"
    - "bad communities with high modularity"

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)