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: "online multi view clustering with incomplete views"
  Abstract: In the era of big data, it is common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view clustering provides a natural way to generate clusters from such data. Since different views share some consistency and complementary information, previous works on multi-view clustering mainly focus on how to combine various numbers of views to improve clustering performance. However, in reality, each view may be incomplete, i.e., instances missing in the view. Furthermore, the size of data could be extremely huge. It is unrealistic to apply multi-view clustering in large real-world applications without considering the incompleteness of views and the memory requirement. None of previous works have addressed all these challenges simultaneously. In this paper, we propose an online multi-view clustering algorithm, OMVC, which deals with large-scale incomplete views. We model the multi-view clustering problem as a joint weighted nonnegative matrix factorization problem and process the multi-view data chunk by chunk to reduce the memory requirement. OMVC learns the latent feature matrices for all the views and pushes them towards a consensus. We further increase the robustness of the learned latent feature matrices in OMVC via lasso regularization. To minimize the influence of incompleteness, dynamic weight setting is introduced to give lower weights to the incoming missing instances in different views. More importantly, to reduce the computational time, we incorporate a faster projected gradient descent by utilizing the Hessian matrices in OMVC. Extensive experiments conducted on four real data demonstrate the effectiveness of the proposed OMVC method.

Connected through hyperedge HE-1 (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"
    - "a survey on multi view clustering"

Connected through hyperedge HE-2 (induced by source paper "weighted clustering ensemble a review"):
  with members:
    - "multi view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources"
    - "locally weighted ensemble clustering"
    - "exhaustive and efficient constraint propagation a graph based learning approach and its applications"

Connected through hyperedge HE-3 (induced by source paper "highly economized multi view binary compression for scalable image clustering"):
  with members:
    - "clustering millions of faces by identity"
    - "a survey on learning to hash"

Connected through hyperedge HE-4 (induced by source paper "a survey on multi view clustering"):
  with members:
    - "a survey of multi view representation learning"
    - "vigan missing view imputation with generative adversarial networks"
    - "deep metric learning via facility location"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "long term recurrent convolutional networks for visual recognition and description"

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)