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: "simnets a generalization of convolutional networks"
  Abstract: We present a deep layered architecture that generalizes classical convolutional neural networks (ConvNets). The architecture, called SimNets, is driven by two operators, one being a similarity function whose family contains the convolution operator used in ConvNets, and the other is a new ”soft max-min-mean” operator called MMECS that realizes classical operators like ReLU and max-pooling, but has additional capabilities that make SimNets a powerful generalization of ConvNets. Two interesting properties that emerge from the architecture are: (i) the basic input to hidden-units to output-nodes machinery contains as special case a kernel machine, and (ii) initializing networks using unsupervised learning is natural. Experiments demonstrate the capability of achieving state of the art accuracy with networks that are 1/8 the size of comparable ConvNets.

Connected through hyperedge HE-1 (induced by source paper "tensorial mixture models"):
  with members:
    - "improving variational inference with inverse autoregressive flow"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "on the expressive power of deep learning a tensor analysis"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "semi supervised learning with deep generative models"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "inductive bias of deep convolutional networks through pooling geometry"):
  with members:
    - "tensorial mixture models"
    - "learning functions when is deep better than shallow"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "convolutional rectifier networks as generalized tensor decompositions"):
  with members:
    - "benefits of depth in neural networks"
    - "the power of depth for feedforward neural networks"
    - "on the expressive power of deep learning a tensor analysis"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"
    - "very deep convolutional networks for large scale image recognition"
    - "on the expressive efficiency of sum product networks"
    - "teaching deep convolutional neural networks to play go"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional networks are hierarchical kernel machines"):
  with members:
    - "on invariance and selectivity in representation learning"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "adaptive normalized risk averting training for deep neural networks"):
  with members:
    - "convolutional kernel networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "network in network"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "theano new features and speed improvements"

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)