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: "clustering on multiple incomplete datasets via collective kernel learning"
  Abstract: Multiple datasets containing different types of features may be available for a given task. For instance, users' profiles can be used to group users for recommendation systems. In addition, a model can also use users' historical behaviors and credit history to group users. Each dataset contains different information and suffices for learning. A number of clustering algorithms on multiple datasets were proposed during the past few years. These algorithms assume that at least one dataset is complete. So far as we know, all the previous methods will not be applicable if there is no complete dataset available. However, in reality, there are many situations where no dataset is complete. As in building a recommendation system, some new users may not have a profile or historical behaviors, while some may not have a credit history. Hence, no available dataset is complete. In order to solve this problem, we propose an approach called Collective Kernel Learning to infer hidden sample similarity from multiple incomplete datasets. The idea is to collectively completes the kernel matrices of incomplete datasets by optimizing the alignment of the shared instances of the datasets. Furthermore, a clustering algorithm is proposed based on the kernel matrix. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The proposed clustering algorithm outperforms the comparison algorithms by as much as two times in normalized mutual information.

Connected through hyperedge HE-1 (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"
    - "online multi view clustering with incomplete views"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning social circles in ego networks based on multi view social graphs"):
  with members:
    - "on the formation of circles in co authorship networks"
    - "community detection in networks with node attributes"
    - "discovering social circles in ego networks"
    - "clustering partially observed graphs via convex optimization"
    - "fast and accurate annotation of short texts with wikipedia pages"

Connected through hyperedge HE-3 (induced by source paper "multi view kernel completion"):
  with members:
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-4 (induced by source paper "online multi view clustering with incomplete views"):
  with members:
    - "non negative sparse coding"

Connected through hyperedge HE-5 (induced by source paper "multi source multi view clustering via discrepancy penalty"):
  with members:
    - "a tutorial on spectral 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)