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 "multiple partitioning of multiplex signed networks application to european parliament votes"):
  Abstract: For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss, and/or 2) they identify groups of antagonistic voters, but not the context associated to their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset.

Includes members:
  Member "signed graph analysis for the interpretation of voting behavior":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "signed graph attention networks")
  Member "brazilian congress structural balance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies")
  Member "relevance of negative links in graph partitioning a case study using votes from the european parliament":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a modelling and computational study of the frustration index in signed networks")
  Member "generalized measures for the evaluation of community detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient community detection in boolean composed multiplex networks")
  Member "synchronization over z2 and community detection in signed multiplex networks with constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the noise sensitivity phase transition in spectral group synchronization over compact groups")
  Member "exploiting social network structure for person to person sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "user profiling using hinge loss markov random fields")
  Member "objective criteria for the evaluation of clustering methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")

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)