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 "network structure exploration via bayesian nonparametric models"):
  Abstract: Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance.

Includes members:
  Member "overlapping community detection in networks with positive and negative links":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization")
  Member "generalized friendship paradox in complex networks the case of scientific collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "friendship paradox biases perceptions in directed networks")
  Member "finding missing edges in networks based on their community structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information filtering by similarity preferential diffusion processes")
  Member "nonparametric link prediction in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dyngraph2vec capturing network dynamics using dynamic graph representation learning")
  Member "an infinite latent attribute model for network data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "collapsed variational bayes inference of infinite relational model")
  Member "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "correlation based community detection")
  Member "experience versus talent shapes the structure of the web":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the evolutions of the rich get richer and the fit get richer phenomena in scholarly networks the case of the strategic management journal")

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)