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: "data fusion by matrix factorization"
  Abstract: For most problems in science and engineering we can obtain data sets that describe the observed system from variousperspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system’s constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneouslyfactorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.

Connected through hyperedge HE-1 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"

Connected through hyperedge HE-2 (induced by source paper "graph embedding on biomedical networks methods applications and evaluations"):
  with members:
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "universal language model fine tuning for text classification"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "predicting multicellular function through multi layer tissue networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-3 (induced by source paper "multi view multi instance multi label learning based on collaborative matrix factorization"):
  with members:
    - "multi source multi view clustering via discrepancy penalty"
    - "a survey on multi view learning"
    - "multi instance multi label learning"

Connected through hyperedge HE-4 (induced by source paper "modeling polypharmacy side effects with graph convolutional networks"):
  with members:
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "complex embeddings for simple link prediction"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "predicting multicellular function through multi layer tissue networks"):
  with members:
    - "gated graph sequence neural networks"
    - "dropout training of matrix factorization and autoencoder for link prediction in sparse graphs"
    - "line large scale information network embedding"
    - "how transferable are features in deep neural networks"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "using the gene ontology hierarchy when predicting gene function"

Connected through hyperedge HE-6 (induced by source paper "sparse deep nonnegative matrix factorization"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "a deep matrix factorization method for learning attribute representations"
    - "deeply learned face representations are sparse selective and robust"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "a unified joint matrix factorization framework for data integration"):
  with members:
    - "non negative matrix factorization with sparseness constraints"

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)