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: "learning representations by maximizing compression"
  Abstract: We give an algorithm that learns a representation of data through compression. The algorithm 1) predicts bits sequentially from those previously seen and 2) has a structure and a number of computations similar to an autoencoder. The likelihood under the model can be calculated exactly, and arithmetic coding can be used directly for compression. When training on digits the algorithm learns filters similar to those of restricted boltzman machines and denoising autoencoders. Independent samples can be drawn from the model by a single sweep through the pixels. The algorithm has a good compression performance when compared to other methods that work under random ordering of pixels.

Connected through hyperedge HE-1 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "learning for video compression"
    - "image and video compression with neural networks a review"
    - "enhancing quality for hevc compressed videos"
    - "on the classification distortion perception tradeoff"
    - "learning based facial image compression with semantic fidelity metric"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"

Connected through hyperedge HE-2 (induced by source paper "neural autoregressive distribution estimation"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "pixel recurrent neural networks"
    - "importance weighted autoencoders"
    - "made masked autoencoder for distribution estimation"
    - "generative moment matching networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "towards machine intelligence"):
  with members:
    - "deep learning in neural networks"
    - "going deeper with convolutions"
    - "deep learning of representations looking forward"
    - "representation learning a review and new perspectives"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "from machine learning to machine reasoning"
    - "simple algorithmic principles of discovery subjective beauty selective attention curiosity creativity"

Connected through hyperedge HE-4 (induced by source paper "made masked autoencoder for distribution estimation"):
  with members:
    - "nice non linear independent components estimation"
    - "deep generative stochastic networks trainable by backprop"
    - "deep autoregressive networks"
    - "sum product networks a new deep architecture"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"

Connected through hyperedge HE-5 (induced by source paper "multi modal sensor registration for vehicle perception via deep neural networks"):
  with members:
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-6 (induced by source paper "deep autoregressive networks"):
  with members:
    - "generalized denoising auto encoders as generative models"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "the arcade learning environment an evaluation platform for general agents"
    - "generating sequences with recurrent neural networks"
    - "a generative process for sampling contractive auto encoders"
    - "variational bayesian inference with stochastic search"
    - "emergence of complex like cells in a temporal product network with local receptive fields"

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.CV(Computer Vision and Pattern Recognition)