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: "creation of a deep convolutional auto encoder in caffe"
  Abstract: The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison with a classic auto-encoder on the example of MNIST dataset.

Connected through hyperedge HE-1 (induced by source paper "supervised and unsupervised end to end deep learning for gene ontology classification of neural in situ hybridization images"):
  with members:
    - "deepbrain functional representation of neural in situ hybridization images for gene ontology classification using deep convolutional autoencoders"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-2 (induced by source paper "daqn deep auto encoder and q network"):
  with members:
    - "curiosity driven exploration by self supervised prediction"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "continuous control with deep reinforcement learning"
    - "deep residual learning for image recognition"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "deep spatial autoencoders for visuomotor learning"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "a deep convolutional auto encoder with pooling unpooling layers in caffe"):
  with members:
    - "importance weighted autoencoders"
    - "caffe convolutional architecture for fast feature embedding"
    - "k sparse autoencoders"

Connected through hyperedge HE-4 (induced by source paper "matrix neural networks"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "neural self talk image understanding via continuous questioning and answering"
    - "symmetry invariant optimization in deep networks"
    - "tensor principal component analysis via sum of squares proofs"
    - "caffe convolutional architecture for fast feature embedding"

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)