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: "energy propagation in deep convolutional neural networks"
  Abstract: Many practical machine learning tasks employ very deep convolutional neural networks. Such large depths pose formidable computational challenges in training and operating the network. It is therefore important to understand how fast the energy contained in the propagated signals (a.k.a. feature maps) decays across layers. In addition, it is desirable that the feature extractor generated by the network be informative in the sense of the only signal mapping to the all-zeros feature vector being the zero input signal. This "trivial null-set" property can be accomplished by asking for "energy conservation" in the sense of the energy in the feature vector being proportional to that of the corresponding input signal. This paper establishes conditions for energy conservation (and thus for a trivial null-set) for a wide class of deep convolutional neural network-based feature extractors and characterizes corresponding feature map energy decay rates. Specifically, we consider general scattering networks employing the modulus non-linearity and we find that under mild analyticity and high-pass conditions on the filters (which encompass, inter alia, various constructions of Weyl-Heisenberg filters, wavelets, ridgelets, ($\alpha$)-curvelets, and shearlets) the feature map energy decays at least polynomially fast. For broad families of wavelets and Weyl-Heisenberg filters, the guaranteed decay rate is shown to be exponential. Moreover, we provide handy estimates of the number of layers needed to have at least $((1-\varepsilon)\cdot 100)\%$ of the input signal energy be contained in the feature vector.

Connected through hyperedge HE-1 (induced by source paper "graph convolutional neural networks via scattering"):
  with members:
    - "encoding robust representation for graph generation"
    - "covariant compositional networks for learning graphs"
    - "dynamic routing between capsules"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "graph based convolutional neural network"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "robust and resource efficient identification of two hidden layer neural networks"):
  with members:
    - "deep neural network approximation theory"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "size independent sample complexity of neural networks"
    - "towards evaluating the robustness of neural networks"
    - "finding a low rank basis in a matrix subspace"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-3 (induced by source paper "on the power of over parametrization in neural networks with quadratic activation"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "learning one hidden layer neural networks with landscape design"
    - "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"
    - "fisher rao metric geometry and complexity of neural networks"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"

Connected through hyperedge HE-4 (induced by source paper "theoretical insights into the optimization landscape of over parameterized shallow neural networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "electron proton dynamics in deep learning"
    - "convergence results for neural networks via electrodynamics"
    - "spectrally normalized margin bounds for neural networks"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-5 (induced by source paper "basic filters for convolutional neural networks training or design"):
  with members:
    - "deep convolutional neural networks on cartoon functions"
    - "discrete deep feature extraction a theory and new architectures"
    - "adam a method for stochastic optimization"
    - "deep scattering spectrum"
    - "unsupervised learning of invariant representations in hierarchical architectures"

Connected through hyperedge HE-6 (induced by source paper "deep convolutional neural networks on cartoon functions"):
  with members:
    - "deep residual learning for image recognition"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "a mathematical theory of deep convolutional neural networks for feature extraction"):
  with members:
    - "deep convolutional neural networks on cartoon functions"
    - "discrete deep feature extraction a theory and new architectures"
    - "deep residual learning for image recognition"
    - "deep convolutional neural networks based on semi discrete frames"
    - "rigid motion scattering for texture classification"
    - "deep roto translation scattering for object classification"
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"

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.IT(Information Theory)