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 kernel method for canonical correlation analysis"
  Abstract: Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine is an efficient approach to improve such a linear method. In this paper, we investigate the effectiveness of applying kernel method to canonical correlation analysis. Keyword: multivariate analysis, multimodal data, kernel method, regularization

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 "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi view intact space learning"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "theory of deep learning iib optimization properties of sgd"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-5 (induced by source paper "deep triplet neural networks with cluster cca for audio visual cross modal retrieval"):
  with members:
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "visual to sound generating natural sound for videos in the wild"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "fully convolutional siamese networks for object tracking"
    - "youtube 8m a large scale video classification benchmark"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-6 (induced by source paper "triplet based deep hashing network for cross modal retrieval"):
  with members:
    - "query adaptive hash code ranking for large scale multi view visual search"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-7 (induced by source paper "deep structured cross modal anomaly detection"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "specae spectral autoencoder for anomaly detection in attributed networks"
    - "learning two branch neural networks for image text matching tasks"
    - "deep structured energy based models for anomaly detection"
    - "learning deep structure preserving image text embeddings"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-8 (induced by source paper "canonical correlation analysis cca based multi view learning an overview"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "deep variational canonical correlation analysis"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "correlational neural networks"
    - "a survey on multi view learning"
    - "manopt a matlab toolbox for optimization on manifolds"

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)