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: "using triangles to improve community detection in directed networks"
  Abstract: In a graph, a community may be loosely defined as a group of nodes that are more closely connected to one another than to the rest of the graph. One common theme is many formalizations is that flows should tend to stay within com- munities. Hence, we expect short cycles to play an impor- tant role. For undirected graphs, the importance of trian- gles - an undirected 3-cycle - has been known for a long time and can be used to improve community detection. To identify communities in directed networks, we propose an undirected edge-weighting scheme based on directed trian- gles. We also propose a new metric on quality of the com- munities that is based on the number of 3-cycles that are split across communities. To demonstrate the impact of our new weighting, we use the standard METIS graph partition- ing tool to determine communities and show experimentally that the resulting communities result in fewer 3-cycles be- ing cut. The magnitude of the e!ect varies between a 10% and 50% reduction. We also find that this weighting scheme improves a task where plausible ground-truth communities are known.

Connected through hyperedge HE-1 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs"):
  with members:
    - "hypergraph cuts with general splitting functions"
    - "clustering in graphs and hypergraphs with categorical edge labels"
    - "learning resolution parameters for graph clustering"
    - "a parallel projection method for metric constrained optimization"
    - "hypergraph clustering with categorical edge labels"
    - "higher order clustering in networks"
    - "motif and hypergraph correlation clustering"
    - "correlation clustering generalized"

Connected through hyperedge HE-2 (induced by source paper "communitygan community detection with generative adversarial nets"):
  with members:
    - "adversarial network embedding"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "gradient estimation using stochastic computation graphs"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-3 (induced by source paper "a graph structured dataset for wikipedia research"):
  with members:
    - "spatio temporal data mining a survey of problems and methods"
    - "mining missing hyperlinks from human navigation traces a case study of wikipedia"

Connected through hyperedge HE-4 (induced by source paper "higher order clustering in heterogeneous information networks"):
  with members:
    - "higher order clustering in networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"
    - "recurrent meta structure for robust similarity measure in heterogeneous information networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"

Connected through hyperedge HE-5 (induced by source paper "new methods for incorporating network cyclic structures to improve community detection"):
  with members:
    - "network clustering and community detection using modulus of families of loops"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "enhancing community detection using a network weighting strategy"

Connected through hyperedge HE-6 (induced by source paper "community detection by information flow simulation"):
  with members:
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "representing big data as networks new methods and insights"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "mapping higher order network flows in memory and multilayer networks with infomap"
    - "higher order organization of complex networks"
    - "can cascades be predicted"
    - "graph based anomaly detection and description a survey"
    - "it doesn t break just on twitter characterizing facebook content during real world events"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-8 (induced by source paper "scalable motif aware graph clustering"):
  with members:
    - "higher order organization of complex networks"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "heat kernel based community detection"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "parallel triangle counting in massive streaming graphs"
    - "a tail bound for read k families of functions"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "defining and evaluating network communities based on ground truth"

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)