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: "trivializing the energy landscape of deep networks"
  Abstract: We study a theoretical model that connects deep learning to finding the ground state of the Hamiltonian of a spherical spin glass. Existing results motivated from statistical physics show that deep networks have a highly non-convex energy landscape with exponentially many local minima and energy barriers beyond which gradient descent algorithms cannot make progress. We leverage a technique known as topology trivialization where, upon perturbation by an external magnetic field, the energy landscape of the spin glass Hamiltonian changes dramatically from exponentially many local minima to "total trivialization", i.e., a constant number of local minima. There also exists a transitional regime with polynomially many local minima which interpolates between these extremes. We show that a number of regularization schemes in deep learning can benefit from this phenomenon. As a consequence, our analysis provides order heuristics to choose regularization parameters and motivates annealing schemes for these perturbations.

Connected through hyperedge HE-1 (induced by source paper "a geometric interpretation of stochastic gradient descent using diffusion metrics"):
  with members:
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "on the emergence of invariance and disentangling in deep representations"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "densely connected convolutional networks"
    - "on the energy landscape of deep networks"

Connected through hyperedge HE-2 (induced by source paper "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "deep learning is robust to massive label noise"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theory iiib generalization in deep networks"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-3 (induced by source paper "regularization for deep learning a taxonomy"):
  with members:
    - "proximal backpropagation"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "deep unsupervised learning through spatial contrasting"
    - "adjusting for dropout variance in batch normalization and weight initialization"
    - "synthesizing robust adversarial examples"
    - "an overview of multi task learning in deep neural networks"
    - "densely connected convolutional networks"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "allennlp a deep semantic natural language processing platform"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "spectrally normalized margin bounds for neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"

Connected through hyperedge HE-5 (induced by source paper "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"):
  with members:
    - "parle parallelizing stochastic gradient descent"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "geometry of optimization and implicit regularization in deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "on the emergence of invariance and disentangling in deep representations"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-6 (induced by source paper "deep relaxation partial differential equations for optimizing deep neural networks"):
  with members:
    - "parle parallelizing stochastic gradient descent"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "identity mappings in deep residual networks"
    - "noisy activation functions"
    - "training recurrent neural networks by diffusion"
    - "on the energy landscape of deep networks"

Connected through hyperedge HE-7 (induced by source paper "entropy sgd biasing gradient descent into wide valleys"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "understanding deep learning requires rethinking generalization"
    - "training recurrent neural networks by diffusion"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "scalable bayesian learning of recurrent neural networks for language modeling"
    - "recurrent batch normalization"
    - "bayesian dark knowledge"

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)