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: "neural network with unbounded activation functions is universal approximator"
  Abstract: Abstract   This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified linear unit (ReLU), which is the new de-facto standard of deep learning. The ReLU network can be analyzed by the ridgelet transform with respect to Lizorkin distributions. By showing three reconstruction formulas by using the Fourier slice theorem, the Radon transform, and Parseval's relation, it is shown that a neural network with unbounded activation functions still satisfies the universal approximation property. As an additional consequence, the ridgelet transform, or the backprojection filter in the Radon domain, is what the network learns after backpropagation. Subject to a constructive admissibility condition, the trained network can be obtained by simply discretizing the ridgelet transform, without backpropagation. Numerical examples not only support the consistency of the admissibility condition but also imply that some non-admissible cases result in low-pass filtering.

Connected through hyperedge HE-1 (induced by source paper "geometric deep learning for computational mechanics part i anisotropic hyperelasticity"):
  with members:
    - "a physics constrained data driven approach based on locally convex reconstruction for noisy database"
    - "a comprehensive survey on graph neural networks"
    - "meta modeling game for deriving theory consistent microstructure based traction separation laws via deep reinforcement learning"
    - "a deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "learning of colors from color names distribution and point estimation"):
  with members:
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "learning to generalize to new compositions in image understanding"
    - "learning to generate compositional color descriptions"
    - "enriching word vectors with subword information"
    - "generative adversarial text to image synthesis"
    - "character sequence models for colorfulwords"
    - "pixel recurrent neural networks"
    - "generating images from captions with attention"

Connected through hyperedge HE-3 (induced by source paper "universal function approximation on graphs using multivalued functions"):
  with members:
    - "a survey on graph kernels"
    - "graph filtration learning"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep learning on graphs a survey"
    - "how powerful are graph neural networks"
    - "understanding deep neural networks with rectified linear units"
    - "graph isomorphism in quasipolynomial time"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "graph neural networks exponentially lose expressive power for node classification"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "on the universality of invariant networks"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "simplifying graph convolutional networks"
    - "stability and generalization of graph convolutional neural networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-5 (induced by source paper "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"):
  with members:
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-6 (induced by source paper "a fine grained spectral perspective on neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "explicitizing an implicit bias of the frequency principle in two layer neural networks"
    - "a mean field theory of batch normalization"
    - "towards characterizing divergence in deep q learning"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-7 (induced by source paper "a simple and efficient architecture for trainable activation functions"):
  with members:
    - "adaptive blending units trainable activation functions for deep neural networks"
    - "self normalizing neural networks"
    - "searching for activation functions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "the power of depth for feedforward neural networks"
    - "deep learning with s shaped rectified linear activation units"

Connected through hyperedge HE-8 (induced by source paper "a function space view of bounded norm infinite width relu nets the multivariate case"):
  with members:
    - "two models of double descent for weak features"
    - "approximation power of random neural networks"
    - "how do infinite width bounded norm networks look in function space"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "neural tangent kernels transportation mappings and universal approximation"
    - "error bounds for approximations with deep relu networks"
    - "understanding deep learning requires rethinking generalization"
    - "why deep neural networks for function approximation"

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.NE(Neural and Evolutionary Computing)