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 neural networks"
  Abstract: Automatically determining the optimal size of a neural network for a given task without prior information currently requires an expensive global search and training many networks from scratch. In this paper, we address the problem of automatically finding a good network size during a single training cycle. We introduce *nonparametric neural networks*, a non-probabilistic framework for conducting optimization over all possible network sizes and prove its soundness when network growth is limited via an L_p penalty. We train networks under this framework by continuously adding new units while eliminating redundant units via an L_2 penalty. We employ a novel optimization algorithm, which we term *adaptive radial-angular gradient descent* or *AdaRad*, and obtain promising results.

Connected through hyperedge HE-1 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method"):
  with members:
    - "bag of words as target for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "exploring sparsity in recurrent neural networks"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-2 (induced by source paper "autogrow automatic layer growing in deep convolutional networks"):
  with members:
    - "cgap continuous growth and pruning for efficient deep learning"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-3 (induced by source paper "learning intrinsic sparse structures within long short term memory"):
  with members:
    - "exploring sparsity in recurrent neural networks"
    - "learning structured sparsity in deep neural networks"
    - "squad 100 000 questions for machine comprehension of text"
    - "learning compact recurrent neural networks"
    - "quasi recurrent neural networks"
    - "recurrent highway networks"
    - "neural architecture search with reinforcement learning"
    - "learning the number of neurons in deep networks"

Connected through hyperedge HE-4 (induced by source paper "lifelong learning with dynamically expandable networks"):
  with members:
    - "progressive neural networks"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "learning structured sparsity in deep neural networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning the number of neurons in deep networks"
    - "adanet adaptive structural learning of artificial neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)