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 "balance in signed bipartite networks"):
  Abstract: A large portion of today's big data can be represented as networks. However, not all networks are the same, and in fact, for many that have additional complexities to their structure, traditional general network analysis methods are no longer applicable. For example, signed networks contain both positive and negative links, and thus dedicated theories and algorithms have been developed. However, previous work mainly focuses on the unipartite setting where signed links connect any pair of nodes. Signed bipartite networks on the one hand, are commonly found, but have primarily been overlooked. Their complexities of having two node types where signed links can only form across the two sets introduce challenges that prevent most existing literature on unipartite signed and unsigned bipartite networks from being applied. On the other hand, balance theory, a key signed social theory, has been generally defined for cycles of any length and is being used in the form of triangles for numerous unipartite signed network tasks. However, in bipartite networks there are no triangles and furthermore there exist two types of nodes. Therefore, in this work, we conduct the first comprehensive analysis and validation of balance theory using the smallest cycle in signed bipartite networks - signed butterflies (i.e., cycles of length 4 containing the two node types). Then, to investigate the applicability of balance theory aiding signed bipartite network tasks, we develop multiple sign prediction methods that utilize balance theory in the form of signed butterflies. Our sign prediction experiment on three real-world signed bipartite networks demonstrates the effectiveness of using these signed butterflies for not only sign prediction, but paves the way for improvements in other signed bipartite network analysis tasks.

Includes members:
  Member "butterfly counting in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "signed network modeling based on structural balance theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "sne signed network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable graph embeddings via sparse transpose proximities")
  Member "measuring and modeling bipartite graphs with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "brazilian congress structural balance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies")
  Member "a survey of signed network mining in social media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "common neighbours and the local community paradigm for topological link prediction in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bipartite link prediction based on topological features via 2 hop path")
  Member "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the ubiquity of web tracking insights from a billion page web crawl")

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)