human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "clustering and community detection in directed networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of community search over big graphs")
  Member "directed closure measures for networks with reciprocity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "measuring directed triadic closure with closure coefficients")
  Member "shaping communities out of triangles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "node centric detection of overlapping communities in social networks")
  Member "community structure and scale free collections of erdős renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "artificial benchmark for community detection abcd fast random graph model with community structure")
  Member "mutual or unrequited love identifying stable clusters in social networks with uni and bi directional links":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tensor spectral clustering for partitioning higher order network structures")

Question: Please tell me which class the source paper 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)