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: "on the evaluation potential of quality functions in community detection for different contexts"
  Abstract: Due to nowadays networks' sizes, the evaluation of a community detection algorithm can only be done using quality functions. These functions measure different networks/graphs structural properties, each of them corresponding to a different definition of a community. Since there exists many definitions for a community, choosing a quality function may be a difficult task, even if the networks' statistics/origins can give some clues about which one to choose.#R##N##R##N#In this paper, we apply a general methodology to identify different contexts, i.e. groups of graphs where the quality functions behave similarly. In these contexts we identify the best quality functions, i.e. quality functions whose results are consistent with expectations from real life applications.

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"
    - "size matters a comparative analysis of community detection algorithms"
    - "graph sketching based massive data clustering"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"
    - "predicting community evolution in social networks"

Connected through hyperedge HE-3 (induced by source paper "community structure characterization"):
  with members:
    - "from community detection to community profiling"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "a community role approach to assess social capitalists visibility in the twitter network"
    - "interpreting communities based on the evolution of a dynamic attributed network"
    - "ged the method for group evolution discovery in social networks"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "metrics for community analysis a survey"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "genperm a unified method for detecting non overlapping and overlapping communities"
    - "z score based modularity for community detection in networks"
    - "permanence and community structure in complex networks"
    - "generalized measures for the evaluation of community detection methods"
    - "generalization of clustering agreements and distances for overlapping clusters and network communities"
    - "leveraging disjoint communities for detecting overlapping community structure"

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)