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: "learning the information divergence"
  Abstract: Information divergence that measures the difference between two nonnegative matrices or tensors has found its use in a variety of machine learning problems. Examples are Nonnegative Matrix/Tensor Factorization, Stochastic Neighbor Embedding, topic models, and Bayesian network optimization. The success of such a learning task depends heavily on a suitable divergence. A large variety of divergences have been suggested and analyzed, but very few results are available for an objective choice of the optimal divergence for a given task. Here we present a framework that facilitates automatic selection of the best divergence among a given family, based on standard maximum likelihood estimation. We first propose an approximated Tweedie distribution for the beta-divergence family. Selecting the best beta then becomes a machine learning problem solved by maximum likelihood. Next, we reformulate alpha-divergence in terms of beta-divergence, which enables automatic selection of alpha by maximum likelihood with reuse of the learning principle for beta-divergence. Furthermore, we show the connections between gamma and beta-divergences as well as R\'enyi and alpha-divergences, such that our automatic selection framework is extended to non-separable divergences. Experiments on both synthetic and real-world data demonstrate that our method can quite accurately select information divergence across different learning problems and various divergence families.

Connected through hyperedge HE-1 (induced by source paper "generalized separable nonnegative matrix factorization"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-2 (induced by source paper "distributionally robust and multi objective nonnegative matrix factorization"):
  with members:
    - "improved svd based initialization for nonnegative matrix factorization using low rank correction"
    - "on the complexity of nonnegative matrix factorization"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-3 (induced by source paper "word embedding based on low rank doubly stochastic matrix decomposition"):
  with members:
    - "large scale distributed neural network training through online distillation"
    - "mittens an extension of glove for learning domain specialized representations"
    - "pointer sentinel mixture models"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "learning discriminative alpha beta divergence for positive definite matrices extended version"):
  with members:
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "a riemannian network for spd matrix learning"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-5 (induced by source paper "amplifying inter message distance on information divergence measures in big data"):
  with members:
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "message importance measure and its application to minority subset detection in big data"
    - "renyi divergence and kullback leibler divergence"
    - "on pinsker s and vajda s type inequalities for csiszar s 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)