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 "common neighbours and the local community paradigm for topological link prediction in bipartite networks"):
  Abstract: Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across the two classes. For instance, modelling the connections between workers and their employers, or electors and parties they vote for, are examples of affiliation networks in social analysis. Ultimately, predicting interactions between products and consumers in personal recommendation systems and market models can provide priceless information for managing cyber-commerce. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Indeed, local state-of-the-art methods for link prediction do not directly exploit the inner bipartite topology, but rather rely on its projection into two one-mode-dimension networks, an example of which is the monopartite network of consumers connected by products and the monopartite network of products connected by consumers. Unfortunately, the one-mode-projections are always less informative than the original bipartite structure. Here, we overcome this theoretical obstacle, and we present a formal definition of common neighbour index (CN) and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilizes the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks, and represent the first attempt to create a local-based formalism that allows to intuitively implement link prediction fully in the bipartite domain.

Includes members:
  Member "evaluating link prediction methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network representation learning for link prediction are we improving upon simple heuristics")
  Member "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "balance in signed bipartite networks")
  Member "efficiently inferring community structure in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extracting hierarchical backbones from bipartite networks")
  Member "solving the apparent diversity accuracy dilemma of recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "the link prediction problem in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bipartite link prediction based on topological features via 2 hop path")

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)