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: "a survey on multi view clustering"
  Abstract: With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rapidly, there has not been a survey to summarize and analyze the current progress. Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches. We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated. To promote future development of MVC, we envision several open problems that may require further investigation and thorough examination.

Connected through hyperedge HE-1 (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"
    - "multi view multiple clustering"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "multi graph fusion for multi view spectral clustering"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "clustering with similarity preserving"
    - "deep sparse subspace clustering"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-3 (induced by source paper "dialog intent induction with deep multi view clustering"):
  with members:
    - "adversarial domain adaptation for duplicate question detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "jointly deep multi view learning for clustering analysis"
    - "prototypical networks for few shot learning"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "multi domain dialog state tracking using recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "feature concatenation multi view subspace clustering"):
  with members:
    - "multi view low rank sparse subspace clustering"
    - "discriminative block diagonal representation learning for image recognition"
    - "visual understanding via multi feature shared learning with global consistency"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "analysis and improvement of low rank representation for subspace segmentation"

Connected through hyperedge HE-5 (induced by source paper "constrained bilinear factorization multi view subspace clustering"):
  with members:
    - "multi view low rank sparse subspace clustering"
    - "discriminative block diagonal representation learning for image recognition"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "one pass incomplete multi view clustering"):
  with members:
    - "doubly aligned incomplete multi view clustering"
    - "incomplete multi view clustering via graph regularized matrix factorization"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "robust localized multi view subspace clustering"
    - "online multi view clustering with incomplete views"

Connected through hyperedge HE-7 (induced by source paper "multiple partitions aligned clustering"):
  with members:
    - "robust graph learning from noisy data"
    - "similarity learning via kernel preserving embedding"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "doubly aligned incomplete multi view clustering"):
  with members:
    - "multi view dynamic facial action unit detection"
    - "robust localized multi view subspace clustering"

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)