human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi view multiple clustering"
  Abstract: Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are designed for single-view data. We assume that the individuality and commonality of multi-view data can be leveraged to generate high-quality and diverse clusterings. To this end, we propose a novel multi-view multiple clustering (MVMC) algorithm. MVMC first adapts multi-view self-representation learning to explore the individuality encoding matrices and the shared commonality matrix of multi-view data. It additionally reduces the redundancy (i.e., enhancing the individuality) among the matrices using the Hilbert-Schmidt Independence Criterion (HSIC), and collects shared information by forcing the shared matrix to be smooth across all views. It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality. We further extend multiple co-clustering on multi-view data and propose a solution called multi-view multiple co-clustering (MVMCC). Our empirical study shows that MVMC (MVMCC) can exploit multi-view data to generate multiple high-quality and diverse clusterings (co-clusterings), with superior performance to the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "structure learning with similarity preserving"):
  with members:
    - "similarity learning via kernel preserving embedding"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep subspace clustering networks"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "top n recommender system via matrix completion"
    - "unsupervised deep embedding for clustering analysis"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-2 (induced by source paper "multi view subspace clustering via partition fusion"):
  with members:
    - "robust graph learning from noisy data"
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "exploring graph learning for semi supervised classification beyond euclidean data"
    - "multiple partitions aligned clustering"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "unified spectral clustering with optimal graph"

Connected through hyperedge HE-3 (induced by source paper "large scale multi view subspace clustering in linear time"):
  with members:
    - "robust graph learning from noisy data"
    - "spectral clustering via ensemble deep autoencoder learning sc edae"
    - "multiple partitions aligned clustering"
    - "clustering with similarity preserving"
    - "efficient solvers for sparse subspace clustering"
    - "multi view low rank sparse subspace clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-4 (induced by source paper "multi view multiple clusterings using deep matrix factorization"):
  with members:
    - "multiple partitions aligned clustering"
    - "a deep matrix factorization method for learning attribute representations"

Question: Please tell me which class the center node 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)