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: "multiple description convolutional neural networks for image compression"
  Abstract: Multiple description coding (MDC) is able to stably transmit the signal in the un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, the traditional MDC doesn't well leverage image's context features to generate multiple descriptions. In this paper, we propose a novel standard-compliant convolutional neural network-based MDC framework in term of image's context features. Firstly, multiple description generator network (MDGN) is designed to produce appearance-similar yet feature-different multiple descriptions automatically according to image's content, which are compressed by standard codec. Secondly, we present multiple description reconstruction network (MDRN) including side reconstruction network (SRN) and central reconstruction network (CRN). When any one of two lossy descriptions is received at the decoder, SRN network is used to improve the quality of this decoded lossy description by removing the compression artifact and up-sampling simultaneously. Meanwhile, we utilize CRN network with two decoded descriptions as inputs for better reconstruction, if both of lossy descriptions are available. Thirdly, multiple description virtual codec network (MDVCN) is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework. Here, two learning algorithms are provided to train our whole framework. In addition to structural similarity loss function, the produced descriptions are used as opposing labels with multiple description distance loss function to regularize the training of MDGN network. These losses guarantee that the generated description images are structurally similar yet finely diverse. Experimental results show a great deal of objective and subjective quality measurements to validate the efficiency of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "concurrently extrapolating and interpolating networks for continuous model generation"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "cfsnet toward a controllable feature space for image restoration"
    - "pixel adaptive convolutional neural networks"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "image super resolution by neural texture transfer"
    - "a general decoupled learning framework for parameterized image operators"
    - "a benchmark for edge preserving image smoothing"

Connected through hyperedge HE-2 (induced by source paper "deep optimized multiple description image coding via scalar quantization learning"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "learning a virtual codec based on deep convolutional neural network to compress image"
    - "understanding convolution for semantic segmentation"
    - "learning convolutional networks for content weighted image compression"
    - "an achievable rate distortion region for multiple descriptions source coding based on coset codes"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "deep multiple description coding by learning scalar quantization"):
  with members:
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "understanding convolution for semantic segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "scalar quantization with random thresholds"

Connected through hyperedge HE-4 (induced by source paper "learning a virtual codec based on deep convolutional neural network to compress image"):
  with members:
    - "enhancing quality for hevc compressed videos"
    - "fast adaptive bilateral filtering"
    - "deep multiple description coding by learning scalar quantization"
    - "optimized pre compensating compression"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "an end to end compression framework based on convolutional neural networks"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "learning convolutional networks for content weighted image compression"

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.MM(Multimedia)