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: "finding community structure in mega scale social networks"
  Abstract: Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. The paper identifies that this inefficiency is caused from merging communities in unbalanced manner. The paper introduces three kinds of metrics (consolidation ratio) to control the process of community analysis trying to balance the sizes of the communities being merged. Three flavors of CNM algorithms are built incorporating those metrics. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. All the methods exhibit dramatic improvement of execution efficiency in comparison with the original CNM algorithm and shows high scalability. The fastest method processes a network with 1 million nodes in 5 minutes and a network with 4 million nodes in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than the original algorithm), finds community structures that has improved modularity, and scales to a network with 5.5 million.

Connected through hyperedge HE-1 (induced by source paper "community detection through likelihood optimization in search of a sound model"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "network structure exploration via bayesian nonparametric models"
    - "clustering and community detection in directed networks a survey"
    - "model of complex networks based on citation dynamics"

Connected through hyperedge HE-2 (induced by source paper "network community detection with a successive spectral relaxation method"):
  with members:
    - "multi level algorithms for modularity clustering"

Connected through hyperedge HE-3 (induced by source paper "metrics for community analysis a survey"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on varying topology of complex networks and performance limitations of community detection algorithms"):
  with members:
    - "tunable and growing network generation model with community structures"
    - "bad communities with high modularity"
    - "empirical comparison of algorithms for network community detection"
    - "a comparison of two techniques for bibliometric mapping multidimensional scaling and vos"

Connected through hyperedge HE-5 (induced by source paper "matching community structure across online social networks"):
  with members:
    - "a local perspective on community structure in multilayer networks"

Connected through hyperedge HE-6 (induced by source paper "a survey of heterogeneous information network analysis"):
  with members:
    - "a survey of rdf data management systems"
    - "organizational chart inference"
    - "predicting neighbor distribution in heterogeneous information networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "integrating heterogeneous information via flexible regularization framework for recommendation"
    - "classification of message spreading in a heterogeneous social network"
    - "scalable topical phrase mining from text corpora"
    - "future influence ranking of scientific literature"

Connected through hyperedge HE-7 (induced by source paper "labelrank a stabilized label propagation algorithm for community detection in networks"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "towards linear time overlapping community detection in social networks"
    - "community detection using a neighborhood strength driven label propagation algorithm"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "extending modularity by capturing the similarity attraction feature in the null model"):
  with members:
    - "data for development the d4d challenge on mobile phone data"
    - "empirical comparison of algorithms for network community detection"
    - "a tutorial on spectral clustering"

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.CY(Computers and Society)