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: "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
  Abstract: The popular Alternating Least Squares (ALS) algorithm for tensor decomposition is extremely efficient, but often converges to poor local optima, particularly when the weights of the factors are non-uniform. We propose a modification of the ALS approach that is as efficient as standard ALS, but provably recovers the true factors with random initialization under standard incoherence assumptions on the factors of the tensor. We demonstrate the significant practical superiority of our approach over traditional ALS (with both random initialization and SVD-based initialization) for a variety of tasks on synthetic data - including tensor factorization on exact, noisy and over-complete tensors, as well as tensor completion - and for computing word embeddings from a third-order word tri-occurrence tensor.

Connected through hyperedge HE-1 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "contrastive multiview coding"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "momentum contrast for unsupervised visual representation learning"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-2 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "stable robust and super fast reconstruction of tensors using multi way projections"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "low rank approximation and regression in input sparsity time"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-3 (induced by source paper "understanding composition of word embeddings via tensor decomposition"):
  with members:
    - "advances in pre training distributed word representations"
    - "tensorflow a system for large scale machine learning"
    - "rand walk a latent variable model approach to word embeddings"
    - "syntax aware multi sense word embeddings for deep compositional models of meaning"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "mathematical foundations for a compositional distributional model of meaning"
    - "most tensor problems are np hard"

Connected through hyperedge HE-4 (induced by source paper "breaking the gridlock in mixture of experts consistent and efficient algorithms"):
  with members:
    - "deep mixture of experts via shallow embedding"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "learning one hidden layer neural networks with landscape design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "attention is all you need"
    - "recovery guarantees for one hidden layer neural networks"

Connected through hyperedge HE-5 (induced by source paper "embedding syntax and semantics of prepositions via tensor decomposition"):
  with members:
    - "ontology aware token embeddings for prepositional phrase attachment"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "embedding lexical features via low rank tensors"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "tensor factorization via matrix factorization"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "polyglot distributed word representations for multilingual nlp"

Connected through hyperedge HE-6 (induced by source paper "exact recovery of low rank tensor decomposition under reshuffling"):
  with members:
    - "describing textures in the wild"

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)