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: "deep convolutional networks are hierarchical kernel machines"
  Abstract: In i-theory a typical layer of a hierarchical architecture consists of HW modules pooling the dot products of the inputs to the layer with the transformations of a few templates under a group. Such layers include as special cases the convolutional layers of Deep Convolutional Networks (DCNs) as well as the non-convolutional layers (when the group contains only the identity). Rectifying nonlinearities -- which are used by present-day DCNs -- are one of the several nonlinearities admitted by i-theory for the HW module. We discuss here the equivalence between group averages of linear combinations of rectifying nonlinearities and an associated kernel. This property implies that present-day DCNs can be exactly equivalent to a hierarchy of kernel machines with pooling and non-pooling layers. Finally, we describe a conjecture for theoretically understanding hierarchies of such modules. A main consequence of the conjecture is that hierarchies of trained HW modules minimize memory requirements while computing a selective and invariant representation.

Connected through hyperedge HE-1 (induced by source paper "simple recurrent units for highly parallelizable recurrence"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "rational recurrences"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "an analysis of neural language modeling at multiple scales"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deriving neural architectures from sequence and graph kernels"
    - "lstm a search space odyssey"
    - "recurrent additive networks"

Connected through hyperedge HE-2 (induced by source paper "deriving neural architectures from sequence and graph kernels"):
  with members:
    - "lstm a search space odyssey"
    - "recurrent additive networks"
    - "long short term memory networks for machine reading"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "gated graph sequence neural networks"
    - "recurrent neural network grammars"
    - "recurrent highway networks"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-3 (induced by source paper "sgd learns the conjugate kernel class of the network"):
  with members:
    - "random features for compositional kernels"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "provable learning of noisy or networks"
    - "convexified convolutional neural networks"
    - "learning halfspaces and neural networks with random initialization"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "steps toward deep kernel methods from infinite neural networks"

Connected through hyperedge HE-4 (induced by source paper "enlightening deep neural networks with knowledge of confounding factors"):
  with members:
    - "convolutional neural networks for attribute based active authentication on mobile devices"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep convolutional inverse graphics network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "complexity theoretic limitations on learning halfspaces"
    - "norm based capacity control in neural networks"
    - "train faster generalize better stability of stochastic gradient descent"
    - "path sgd path normalized optimization in deep neural networks"
    - "on the quality of the initial basin in overspecified neural networks"

Connected through hyperedge HE-6 (induced by source paper "why and when can deep but not shallow networks avoid the curse of dimensionality a review"):
  with members:
    - "deep vs shallow networks an approximation theory perspective"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "on the expressive power of deep learning a tensor analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "on the expressive power of deep learning a tensor analysis"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "statistical machine translation features with multitask tensor networks"
    - "an average case depth hierarchy theorem for boolean circuits"
    - "very deep convolutional networks for large scale image recognition"
    - "on the expressive efficiency of sum product networks"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

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)