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: "nonparametric canonical correlation analysis"
  Abstract: Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel or neural network structure), and are computationally demanding. Interestingly, the theory of nonlinear CCA, without functional restrictions, had been studied in the population setting by Lancaster already in the 1950s, but these results have not inspired practical algorithms. We revisit Lancaster's theory to devise a practical algorithm for nonparametric CCA (NCCA). Specifically, we show that the solution can be expressed in terms of the singular value decomposition of a certain operator associated with the joint density of the views. Thus, by estimating the population density from data, NCCA reduces to solving an eigenvalue system, superficially like kernel CCA but, importantly, without requiring the inversion of any kernel matrix. We also derive a partially linear CCA (PLCCA) variant in which one of the views undergoes a linear projection while the other is nonparametric. Using a kernel density estimate based on a small number of nearest neighbors, our NCCA and PLCCA algorithms are memory-efficient, often run much faster, and perform better than kernel CCA and comparable to deep CCA.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a general scoring rule for randomized kernel approximation with application to canonical correlation analysis"):
  with members:
    - "on sampling random features from empirical leverage scores implementation and theoretical guarantees"
    - "but how does it work in theory linear svm with random features"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "data driven random fourier features using stein effect"
    - "stochastic approximation for canonical correlation analysis"
    - "orthogonal random features"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

Connected through hyperedge HE-3 (induced by source paper "kernelized multiview subspace analysis by self weighted learning"):
  with members:
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "predicting visual features from text for image and video caption retrieval"

Connected through hyperedge HE-4 (induced by source paper "task driven common representation learning via bridge neural network"):
  with members:
    - "linking image and text with 2 way nets"
    - "learning deep structure preserving image text embeddings"
    - "a survey on multi view learning"

Connected through hyperedge HE-5 (induced by source paper "unsupervised correlation analysis"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "the role of minimal complexity functions in unsupervised learning of semantic mappings"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised learning of semantic mappings"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "one sided unsupervised domain mapping"
    - "word translation without parallel data"

Connected through hyperedge HE-6 (induced by source paper "non linear canonical correlation analysis a compressed representation approach"):
  with members:
    - "bregman divergence bounds and universality properties of the logarithmic loss"
    - "on the universality of the logistic loss function"
    - "stochastic approximation for canonical correlation analysis"
    - "on deep multi view representation learning objectives and optimization"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-7 (induced by source paper "multiview kernels for low dimensional modeling of seismic events"):
  with members:
    - "multi view kernel consensus for data analysis"
    - "multiview diffusion maps"

Connected through hyperedge HE-8 (induced by source paper "an em based probabilistic two dimensional cca with application to face recognition"):
  with members:
    - "canonical correlation analysis of high dimensional data with very small sample support"

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)