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 "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications"):
  Abstract: Abstract In this article, we extend several algebraic graph analysis methods to bipartite networks. In various areas of science, engineering, and commerce, many types of information can be represented as networks, and thus, the discipline of network analysis plays an important role in these domains. A powerful and widespread class of network analysis methods is based on algebraic graph theory, i.e., representing graphs as square adjacency matrices. However, many networks are of a very specific form that clashes with that representation: they are bipartite. That is, they consist of two node types, with each edge connecting a node of one type with a node of the other type. Examples of bipartite networks (also called two-mode networks) are persons and the social groups they belong to, musical artists and the musical genres they play, and text documents and the words they contain. In fact, any type of feature that can be represented by a categorical variable can be interpreted as a bipartite network. Although...

Includes members:
  Member "empirical analysis of predictive algorithms for collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "bipartite link prediction based on topological features via 2 hop path")
  Member "the slashdot zoo mining a social network with negative edges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep network embedding for graph representation learning in signed networks")
  Member "collaborative filtering based on multi channel diffusion":
    also connected through other hyperedges:
      (none)
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "recommender system for online dating service":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns")

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.DM(Discrete Mathematics)