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 "relational thematic clustering with mutually preferred neighbors"):
  Abstract: Automatically learning thematic clusters in network data has long been a challenging task in machine learning community. A number of approaches have been proposed to accomplish it, utilizing edges, vertex features, or both aforementioned. However, few of them consider how the quantification of dichotomous inclination w.r.t. network topology and vertex features may influence vertex-cluster preferences, which deters previous methods from uncovering more interpretable latent groups in network data. To fill this void, we propose a novel probabilistic model, dubbed Relational Thematic Clustering with Mutually Preferred Neighbors (RTCMPN). Different from prevalent approaches which predetermine the learning significance of edge structure and vertex features, RTCMPN can further learn the latent preferences indicating which neighboring vertices are more possible to be in the same cluster, and the dichotomous inclinations describing how relative significance w.r.t. edge structure and vertex features may impact the association between pairwise vertices. Therefore, cluster structure implanted with edge structure, vertex features, neighboring preferences, and vertex-vertex dichotomous inclinations can be learned by RTCMPN. We additionally derive an effective Expectation-Maximization algorithm for RTCMPN to infer the optimal model parameters. RTCMPN has been compared with several strong baselines on various network data. The remarkable results validate the effectiveness of RTCMPN.

Includes members:
  Member "gromov wasserstein factorization models for graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning autoencoders with relational regularization")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "discovering social circles in ego networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "community detection in node attributed social networks a survey")

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)