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: "convolutional rectifier networks as generalized tensor decompositions"
  Abstract: Convolutional rectifier networks, i.e. convolutional neural networks with rectified linear activation and max or average pooling, are the cornerstone of modern deep learning. However, despite their wide use and success, our theoretical understanding of the expressive properties that drive these networks is partial at best. On the other hand, we have a much firmer grasp of these issues in the world of arithmetic circuits. Specifically, it is known that convolutional arithmetic circuits possess the property of "complete depth efficiency", meaning that besides a negligible set, all functions that can be implemented by a deep network of polynomial size, require exponential size in order to be implemented (or even approximated) by a shallow network. In this paper we describe a construction based on generalized tensor decompositions, that transforms convolutional arithmetic circuits into convolutional rectifier networks. We then use mathematical tools available from the world of arithmetic circuits to prove new results. First, we show that convolutional rectifier networks are universal with max pooling but not with average pooling. Second, and more importantly, we show that depth efficiency is weaker with convolutional rectifier networks than it is with convolutional arithmetic circuits. This leads us to believe that developing effective methods for training convolutional arithmetic circuits, thereby fulfilling their expressive potential, may give rise to a deep learning architecture that is provably superior to convolutional rectifier networks but has so far been overlooked by practitioners.

Connected through hyperedge HE-1 (induced by source paper "tensor decompositions in deep learning"):
  with members:
    - "theoretically expressive and edge aware graph learning"
    - "tucker tensor layer in fully connected neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "bayesian tensor factorisation for bottom up hidden tree markov models"
    - "a gentle introduction to deep learning for graphs"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "how powerful are graph neural networks"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "stability and generalization of graph convolutional neural networks"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "unifying local and non local signal processing with graph cnns"
    - "deriving neural architectures from sequence and graph kernels"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "tight sample complexity of learning one hidden layer convolutional neural networks"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-5 (induced by source paper "einconv exploring unexplored tensor decompositions for convolutional neural networks"):
  with members:
    - "resource efficient 3d convolutional neural networks"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "wide compression tensor ring nets"
    - "efficient neural architecture search via parameter sharing"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-6 (induced by source paper "tucker decomposition network expressive power and comparison"):
  with members:
    - "expressive power of recurrent neural networks"
    - "benefits of depth in neural networks"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the expressive power of deep learning a tensor analysis"
    - "going deeper with convolutions"
    - "on the expressive efficiency of sum product networks"

Connected through hyperedge HE-7 (induced by source paper "universality theorems for generative models"):
  with members:
    - "generalized tensor models for recurrent neural networks"
    - "geometry score a method for comparing generative adversarial networks"
    - "the mechanics of n player differentiable games"
    - "disconnected manifold learning for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the expressive power of neural networks a view from the width"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "deep neural network approximation theory"):
  with members:
    - "analysis of the generalization error empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of black scholes partial differential equations"
    - "optimal approximation with sparsely connected deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "why deep neural networks for function approximation"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "deep residual learning for image recognition"

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)