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: "online tensor methods for learning latent variable models"
  Abstract: We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomposition of moment tensors using stochastic gradient descent. We conduct optimization of multilinear operations in SGD and avoid directly forming the tensors, to save computational and storage costs. We present optimized algorithm in two platforms. Our GPU-based implementation exploits the parallelism of SIMD architectures to allow for maximum speed-up by a careful optimization of storage and data transfer, whereas our CPU-based implementation uses efficient sparse matrix computations and is suitable for large sparse datasets. For the community detection problem, we demonstrate accuracy and computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic modeling problem, we also demonstrate good performance on the New York Times dataset. We compare our results to the state-of-the-art algorithms such as the variational method, and report a gain of accuracy and a gain of several orders of magnitude in the execution time.

Connected through hyperedge HE-1 (induced by source paper "understanding generalization in deep learning via tensor methods"):
  with members:
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "generalization error in deep learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "tensor contraction layers for parsimonious deep nets"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-2 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition"):
  with members:
    - "detecting overlapping communities from local spectral subspaces"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "a tensor approach to learning mixed membership community models"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "community detection in networks with node attributes"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "spectralfpl online spectral learning for single topic models"):
  with members:
    - "spectral methods for nonparametric models"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "spectral experts for estimating mixtures of linear regressions"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "a unified optimization approach for sparse tensor operations on gpus"):
  with members:
    - "parallel tensor compression for large scale scientific data"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-5 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "convolutional dictionary learning through tensor factorization"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "adversarial top k ranking"):
  with members:
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "tensor decompositions for learning latent variable models"
    - "rank centrality ranking from pair wise comparisons"
    - "results on the redundancy of universal compression for finite length sequences"

Connected through hyperedge HE-7 (induced by source paper "decomposing overcomplete 3rd order tensors using sum of squares algorithms"):
  with members:
    - "learning mixtures of gaussians in high dimensions"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "rounding sum of squares relaxations"
    - "smoothed analysis of tensor decompositions"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"

Connected through hyperedge HE-8 (induced by source paper "provable tensor methods for learning mixtures of generalized linear models"):
  with members:
    - "sample complexity analysis for learning overcomplete latent variable models through tensor methods"
    - "learning mixtures of ranking models"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "score function features for discriminative learning matrix and tensor framework"
    - "fast detection of overlapping communities via online tensor methods"
    - "discriminative features via generalized eigenvectors"
    - "learning mixtures of linear classifiers"
    - "nonparametric estimation of multi view latent variable models"

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)