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: "multiview diffusion maps"
  Abstract: In this study we consider learning a reduced dimensionality representation from datasets obtained under multiple views. Such multiple views of datasets can be obtained, for example, when the same underlying process is observed using several different modalities, or measured with different instrumentation. Our goal is to effectively exploit the availability of such multiple views for various purposes, such as non-linear embedding, manifold learning, spectral clustering, anomaly detection and non-linear system identification. Our proposed method exploits the intrinsic relation within each view, as well as the mutual relations between views. We do this by defining a cross-view model, in which an implied Random Walk process between objects is restrained to hop between the different views. Our method is robust to scaling of each dataset, and is insensitive to small structural changes in the data. Within this framework, we define new diffusion distances and analyze the spectra of the implied kernels. We demonstrate the applicability of the proposed approach on both artificial and real data sets.

Connected through hyperedge HE-1 (induced by source paper "visualizing the phate of neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "understanding deep learning requires rethinking generalization"
    - "improved techniques for training gans"
    - "adam a method for stochastic optimization"
    - "qualitatively characterizing neural network optimization problems"

Connected through hyperedge HE-2 (induced by source paper "multiview kernels for low dimensional modeling of seismic events"):
  with members:
    - "multi view kernel consensus for data analysis"
    - "nonparametric canonical correlation analysis"

Connected through hyperedge HE-3 (induced by source paper "kernel scaling for manifold learning and classification"):
  with members:
    - "multiview kernels for low dimensional modeling of seismic events"
    - "multi view kernel consensus for data analysis"
    - "multi view kernel consensus for data analysis and signal processing"
    - "danco dimensionality from angle and norm concentration"

Connected through hyperedge HE-4 (induced by source paper "local canonical correlation analysis for nonlinear common variables discovery"):
  with members:
    - "nonparametric canonical correlation analysis"
    - "two manifold problems with applications to nonlinear system identification"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-5 (induced by source paper "multimodal latent variable analysis"):
  with members:
    - "local canonical correlation analysis for nonlinear common variables discovery"
    - "nonparametric canonical correlation analysis"
    - "manifold relevance determination"
    - "two manifold problems with applications to nonlinear system identification"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "kernel based sensor fusion with application to audio visual voice activity detection"):
  with members:
    - "nonparametric canonical correlation analysis"
    - "a filtering technique for markov chains with applications to spectral embedding"
    - "making laplacians commute"
    - "two manifold problems with applications to nonlinear system identification"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "multi view kernel consensus for data analysis"):
  with members:
    - "two manifold problems with applications to nonlinear system identification"
    - "a kernel method for canonical correlation analysis"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

Connected through hyperedge HE-8 (induced by source paper "multidimensional scaling on multiple input distance matrices"):
  with members:
    - "a survey on multi view learning"

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)