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 "hidden community detection in social networks"):
  Abstract: We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidden community structure. #R##N#We present a novel approach called HICODE (HIdden COmmunity DEtection) that identifies the hidden community structure as well as the dominant community structure. By weakening the strength of the dominant structure, one can uncover the hidden structure beneath. Likewise, by reducing the strength of the hidden structure, one can more accurately identify the dominant structure. In this way, HICODE tackles both tasks simultaneously. #R##N#Extensive experiments on real-world networks demonstrate that HICODE outperforms several state-of-the-art community detection methods in uncovering both the dominant and the hidden structure. In the Facebook university social networks, we find multiple non-redundant sets of communities that are strongly associated with residential hall, year of registration or career position of the faculties or students, while the state-of-the-art algorithms mainly locate the dominant ground truth category. In the Due to the difficulty of labeling all ground truth communities in real-world datasets, HICODE provides a promising approach to pinpoint the existing latent communities and uncover communities for which there is no ground truth. Finding this unknown structure is an extremely important community detection problem.

Includes members:
  Member "detecting overlapping communities from local spectral subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "revealing multiple layers of hidden community structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hidden community detection on two layer stochastic models a theoretical prospective")
  Member "deep community detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications")
  Member "heat kernel based community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "connectivity driven communication in multi agent reinforcement learning through diffusion processes on graphs")
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning with relational information for short sequences")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "social structure of facebook networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "demon a local first discovery method for overlapping communities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks")

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)