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 "multi graph fusion for multi view spectral clustering"):
  Abstract: A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, how to fuse different views into one graph. More often than not, the similarities between samples may be manifested differently by different views. Many existing algorithms either simply take the average of multiple views or just learn a common graph. These simple approaches fail to consider the flexible local manifold structures of all views. Hence, the rich heterogeneous information is not fully exploited. Second, how to learn the explicit cluster structure. Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately. Those unreliable graphs might lead to suboptimal clustering results. To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously. The fusion graph approximates the original graph of each individual view but maintains an explicit cluster structure. Experiments on four widely used data sets confirm the superiority of the proposed method.

Includes members:
  Member "robust graph learning from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "outlier detection ensemble with embedded feature selection")
  Member "low rank kernel learning for graph based clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clustering with similarity preserving")
  Member "robust subspace discovery by block diagonal adaptive locality constrained representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space")
  Member "clustering with similarity preserving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large scale multi view subspace clustering in linear time")
  Member "deep sparse subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised convolutional subspace clustering network")
  Member "twin learning for similarity and clustering a unified kernel approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structure learning with similarity preserving")
  Member "a survey on multi view clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi view subspace clustering via partition fusion")
  Member "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a bert based sentiment analysis and key entity detection approach for online financial texts")

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.LG(Machine Learning)