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: "overlapping community detection in networks with positive and negative links"
  Abstract: Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real-world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperforms other state-of-the-art models at shedding light on the community detection in synthetic signed networks.

Connected through hyperedge HE-1 (induced by source paper "on equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization"):
  with members:
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse networks"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "detection and localization of change points in temporal networks with the aid of stochastic block models"):
  with members:
    - "model selection for degree corrected block models"
    - "detecting change points in the large scale structure of evolving networks"

Connected through hyperedge HE-3 (induced by source paper "a survey of signed network mining in social media"):
  with members:
    - "signed link analysis in social media networks"
    - "stochastic block model and exploratory analysis in signed networks"
    - "signed graph embedding when everybody can sit closer to friends than enemies"
    - "negative link prediction in social media"
    - "applications of structural balance in signed social networks"
    - "breaking the small cluster barrier of graph clustering"
    - "prediction and clustering in signed networks a local to global perspective"
    - "sitting closer to friends than enemies revisited"

Connected through hyperedge HE-4 (induced by source paper "network structure exploration via bayesian nonparametric models"):
  with members:
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "finding missing edges in networks based on their community structure"
    - "nonparametric link prediction in dynamic networks"
    - "an infinite latent attribute model for network data"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"
    - "experience versus talent shapes the structure of the web"

Connected through hyperedge HE-5 (induced by source paper "leveraging disjoint communities for detecting overlapping community structure"):
  with members:
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"

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)