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: "sparse activity and sparse connectivity in supervised learning"
  Abstract: Sparseness is a useful regularizer for learning in a wide range of applications, in particular in neural networks. This paper proposes a model targeted at classification tasks, where sparse activity and sparse connectivity are used to enhance classification capabilities. The tool for achieving this is a sparseness-enforcing projection operator which finds the closest vector with a pre-defined sparseness for any given vector. In the theoretical part of this paper, a comprehensive theory for such a projection is developed. In conclusion, it is shown that the projection is differentiable almost everywhere and can thus be implemented as a smooth neuronal transfer function. The entire model can hence be tuned end-to-end using gradient-based methods. Experiments on the MNIST database of handwritten digits show that classification performance can be boosted by sparse activity or sparse connectivity. With a combination of both, performance can be significantly better compared to classical non-sparse approaches.

Connected through hyperedge HE-1 (induced by source paper "shakeout a new approach to regularized deep neural network training"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "hcp a flexible cnn framework for multi label image classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "improved dropout for shallow and deep learning"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "sparseout controlling sparsity in deep networks"):
  with members:
    - "bridgeout stochastic bridge regularization for deep neural networks"
    - "regularizing and optimizing lstm language models"
    - "wide residual networks"
    - "pointer sentinel mixture models"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "a critical review of recurrent neural networks for sequence learning"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning halfspaces and neural networks with random initialization"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "efficient learning of linear separators under bounded noise"
    - "ell_1 regularized neural networks are improperly learnable in polynomial time"
    - "norm based capacity control in neural networks"
    - "provable methods for training neural networks with sparse connectivity"
    - "provable bounds for learning some deep representations"
    - "from average case complexity to improper learning complexity"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-4 (induced by source paper "efficient dictionary learning with sparseness enforcing projections"):
  with members:
    - "block coordinate descent for sparse nmf"
    - "separable dictionary learning"
    - "feature grouping from spatially constrained multiplicative interaction"
    - "estimating unknown sparsity in compressed sensing"
    - "learning sparse representations of depth"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "comparing measures of sparsity"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-5 (induced by source paper "ell_1 regularized neural networks are improperly learnable in polynomial time"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "the loss surfaces of multilayer networks"
    - "provable methods for training neural networks with sparse connectivity"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "provable bounds for learning some deep representations"
    - "visualizing and understanding convolutional networks"
    - "building high level features using large scale unsupervised learning"

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)