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 "ensemble based overlapping community detection using disjoint community structures"):
  Abstract: While there has been a plethora of approaches for detecting disjoint communities from real-world complex networks, some methods for detecting overlapping community structures have also been recently proposed. In this work, we argue that, instead of developing separate approaches for detecting overlapping communities, a promising alternative is to infer the overlapping communities from multiple disjoint community structures. We propose an ensemble-based approach, called EnCoD, that leverages the solutions produced by various disjoint community detection algorithms to discover the overlapping community structure. Specifically, EnCoD generates a feature vector for each vertex from the results of the base algorithms and learns which features lead to detect densely connected overlapping regions in an unsupervised way. It keeps on iterating until the likelihood of each vertex belonging to its own community maximizes. Experiments on both synthetic and several real-world networks (with known ground-truth community structures) reveal that EnCoD significantly outperforms nine state-of-the-art overlapping community detection algorithms. Finally, we show that EnCoD is generic enough to be applied to networks where the vertices are associated with explicit semantic features. To the best of our knowledge, EnCoD is the second ensemble-based overlapping community detection approach after MEDOC [1].

Includes members:
  Member "dyperm maximizing permanence for dynamic community detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamo dynamic community detection by incrementally maximizing modularity")
  Member "iedc an integrated approach for overlapping and non overlapping community detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach")
  Member "ensemble based algorithms to detect disjoint and overlapping communities in networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ensemble based discovery of disjoint overlapping and fuzzy community structures in networks")
  Member "metrics for community analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when less is more systematic analysis of cascade based community detection")
  Member "think locally act locally detection of small medium sized and large communities in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "migration networks applications of network analysis to large scale human mobility")
  Member "on the formation of circles in co authorship networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cone community oriented network embedding")
  Member "leveraging disjoint communities for detecting overlapping community structure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "high quality disjoint and overlapping community structure in large scale complex networks")
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning with relational information for short sequences")

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)