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: "evidential communities for complex networks"
  Abstract: Community detection is of great importance for understanding graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communities in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropriate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure.

Connected through hyperedge HE-1 (induced by source paper "the belief noisy or model applied to network reliability analysis"):
  with members:
    - "ecmdd evidential c medoids clustering with multiple prototypes"
    - "median evidential c means algorithm and its application to community detection"
    - "a similarity based community detection method with multiple prototype representation"
    - "evidential relational clustering using medoids"
    - "evidential em algorithm applied to progressively censored observations"
    - "a generalization of the noisy or model"
    - "the application of the matrix calculus to belief functions"

Connected through hyperedge HE-2 (induced by source paper "ecmdd evidential c medoids clustering with multiple prototypes"):
  with members:
    - "adaptive imputation of missing values for incomplete pattern classification"
    - "median evidential c means algorithm and its application to community detection"
    - "evidential em algorithm applied to progressively censored observations"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"
    - "decision support with belief functions theory for seabed characterization"

Connected through hyperedge HE-3 (induced by source paper "evidential label propagation algorithm for graphs"):
  with members:
    - "median evidential c means algorithm and its application to community detection"
    - "a similarity based community detection method with multiple prototype representation"
    - "evidential relational clustering using medoids"
    - "evidential em algorithm applied to progressively censored observations"

Connected through hyperedge HE-4 (induced by source paper "evidential relational clustering using medoids"):
  with members:
    - "median evidential c means algorithm and its application to community detection"
    - "evidential em algorithm applied to progressively censored observations"

Connected through hyperedge HE-5 (induced by source paper "median evidential c means algorithm and its application to community detection"):
  with members:
    - "decision support with belief functions theory for seabed characterization"

Connected through hyperedge HE-6 (induced by source paper "a similarity based community detection method with multiple prototype representation"):
  with members:
    - "median evidential c means algorithm and its application to community detection"

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)