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: "semantic perceptual image compression using deep convolution networks"
  Abstract: It has long been considered a significant problem to improve the visual quality of lossy image and video compression. Recent advances in computing power together with the availability of large training data sets has increased interest in the application of deep learning cnns to address image recognition and image processing tasks. Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression. A modest increase in complexity is incorporated to the encoder which allows a standard, off-the-shelf jpeg decoder to be used. While jpeg encoding may be optimized for generic images, the process is ultimately unaware of the specific content of the image to be compressed. Our technique makes jpeg content-aware by designing and training a model to identify multiple semantic regions in a given image. Unlike object detection techniques, our model does not require labeling of object positions and is able to identify objects in a single pass. We present a new cnn architecture directed specifically to image compression, which generates a map that highlights semantically-salient regions so that they can be encoded at higher quality as compared to background regions. By adding a complete set of features for every class, and then taking a threshold over the sum of all feature activations, we generate a map that highlights semantically-salient regions so that they can be encoded at a better quality compared to background regions. Experiments are presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset, in which our algorithm achieves higher visual quality for the same compressed size.

Connected through hyperedge HE-1 (induced by source paper "learning for video compression"):
  with members:
    - "learning based facial image compression with semantic fidelity metric"
    - "convolutional neural network based block up sampling for intra frame coding"
    - "an end to end compression framework based on convolutional neural networks"
    - "a convolutional neural network approach for half pel interpolation in video coding"
    - "a convolutional neural network approach for post processing in hevc intra coding"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "neural network based arithmetic coding of intra prediction modes in hevc"
    - "learning to inpaint for image compression"

Connected through hyperedge HE-2 (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-3 (induced by source paper "neural image compression and explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining a black box by using a deep variational information bottleneck approach"
    - "global model interpretation via recursive partitioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-4 (induced by source paper "learning based facial image compression with semantic fidelity metric"):
  with members:
    - "learning for video compression"
    - "conditional probability models for deep image compression"
    - "an end to end compression framework based on convolutional neural networks"
    - "guetzli perceptually guided jpeg encoder"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "learning to inpaint for image compression"
    - "learning convolutional networks for content weighted image compression"
    - "full resolution image compression with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "machine learning for error correction with natural redundancy"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "end to end learning of communications systems without a channel model"
    - "robustness of neural networks against storage media errors"
    - "learning convolutional networks for content weighted image compression"
    - "fully convolutional networks for semantic segmentation"
    - "a new approach to content based file type detection"

Connected through hyperedge HE-6 (induced by source paper "coconet a deep neural network for mapping pixel coordinates to color values"):
  with members:
    - "spatially adaptive image compression using a tiled deep network"
    - "optimizing the trade off between single stage and two stage object detectors using image difficulty prediction"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"
    - "generic 3d convolutional fusion for image restoration"

Connected through hyperedge HE-7 (induced by source paper "deflecting adversarial attacks with pixel deflection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "detecting adversarial attacks on neural network policies with visual foresight"
    - "countering adversarial images using input transformations"
    - "defense against universal adversarial perturbations"

Connected through hyperedge HE-8 (induced by source paper "protecting jpeg images against adversarial attacks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deflecting adversarial attacks with pixel deflection"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "countering adversarial images using input transformations"
    - "end to end learning for self driving cars"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

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)