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: "a deep convolutional auto encoder with pooling unpooling layers in caffe"
  Abstract: This paper presents the development of several models of a deep convolutional auto-encoder in the Caffe deep learning framework and their experimental evaluation on the example of MNIST dataset. We have created five models of a convolutional auto-encoder which differ architecturally by the presence or absence of pooling and unpooling layers in the auto-encoder's encoder and decoder parts. Our results show that the developed models provide very good results in dimensionality reduction and unsupervised clustering tasks, and small classification errors when we used the learned internal code as an input of a supervised linear classifier and multi-layer perceptron. The best results were provided by a model where the encoder part contains convolutional and pooling layers, followed by an analogous decoder part with deconvolution and unpooling layers without the use of switch variables in the decoder part. The paper also discusses practical details of the creation of a deep convolutional auto-encoder in the very popular Caffe deep learning framework. We believe that our approach and results presented in this paper could help other researchers to build efficient deep neural network architectures in the future.

Connected through hyperedge HE-1 (induced by source paper "thumbnet one thumbnail image contains all you need for recognition"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "demystifying neural style transfer"
    - "thinet a filter level pruning method for deep neural network compression"
    - "learning a wavelet like auto encoder to accelerate deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "pruning filters for efficient convnets"
    - "tensorflow a system for large scale machine learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (induced by source paper "soft autoencoder and its wavelet shrinkage interpretation"):
  with members:
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "fuzzy logic interpretation of artificial neural networks"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "a new type of neurons for machine learning"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"

Connected through hyperedge HE-3 (induced by source paper "deep unsupervised multi view detection of video game stream highlights"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "convolutional two stream network fusion for video action recognition"
    - "learning representations of affect from speech"
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "very deep convolutional networks for large scale image recognition"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "understanding and representing the semantics of large structured documents"):
  with members:
    - "understanding the logical and semantic structure of large documents"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "generating news headlines with recurrent neural networks"
    - "modeling documents with deep boltzmann machines"

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)