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 of multi view representation learning"
  Abstract: Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces two categories for multi-view representation learning: multi-view representation alignment and multi-view representation fusion. Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment. Representative examples are canonical correlation analysis (CCA) and its several extensions. Then, from the perspective of representation fusion, we investigate the advancement of multi-view representation learning that ranges from generative methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to neural network-based methods including multi-modal autoencoders, multi-view convolutional neural networks, and multi-modal recurrent neural networks. Further, we also investigate several important applications of multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical foundation and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.

Connected through hyperedge HE-1 (induced by source paper "contrastive multiview coding"):
  with members:
    - "learning robust and multilingual speech representations"
    - "self supervised learning of video induced visual invariances"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "randaugment practical data augmentation with no separate search"
    - "data efficient image recognition with contrastive predictive coding"
    - "fast autoaugment"

Connected through hyperedge HE-2 (induced by source paper "learning joint acoustic phonetic word embeddings"):
  with members:
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "sampling matters in deep embedding learning"
    - "learning word like units from joint audio visual analysis"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "query by example search with discriminative neural acoustic word embeddings"
    - "learning word embeddings from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-3 (induced by source paper "unsupervised co learning on mathcal g manifolds across irreducible representations"):
  with members:
    - "cryo electron microscopy image analysis using multi frequency vector diffusion maps"
    - "multi frequency phase synchronization"
    - "multi frequency vector diffusion maps"
    - "a study of the classification of low dimensional data with supervised manifold learning"
    - "fast steerable principal component analysis"
    - "a survey on multi view learning"
    - "exact matrix completion via convex optimization"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "multi view locality low rank embedding for dimension reduction"):
  with members:
    - "auto weighted mutli view sparse reconstructive embedding"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "co regularized multi view sparse reconstruction embedding for dimension reduction"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "a survey on multi view learning"

Connected through hyperedge HE-5 (induced by source paper "a multi view dimensionality reduction algorithm based on smooth representation model"):
  with members:
    - "multi view intact space learning"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "correlation adaptive subspace segmentation by trace lasso"

Connected through hyperedge HE-6 (induced by source paper "multi view information theoretic co clustering for co occurrence data"):
  with members:
    - "co clustering via information theoretic markov aggregation"
    - "a survey on multi view learning"

Connected through hyperedge HE-7 (induced by source paper "online learning a comprehensive survey"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "online deep learning learning deep neural networks on the fly"
    - "an online hierarchical algorithm for extreme clustering"
    - "the scaling limit of high dimensional online independent component analysis"
    - "efficient second order online learning by sketching"
    - "a framework of sparse online learning and its applications"
    - "second order quantile methods for experts and combinatorial games"

Connected through hyperedge HE-8 (induced by source paper "correlated embeddings of pairs of dependent random variables"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "an efficient approach to informative feature extraction from multimodal data"
    - "deep generalized canonical correlation analysis"
    - "a generalized power iteration method for solving quadratic problem on the stiefel manifold"
    - "deep variational canonical correlation analysis"
    - "principal inertia components and applications"
    - "tensorflow a system for large scale machine learning"
    - "linear bounds between contraction coefficients for f divergences"

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)