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: "variable rate image compression with recurrent neural networks"
  Abstract: A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements. Due to these factors, it has become the norm for modern graphics-heavy websites to transmit low-resolution, low-bytecount image previews (thumbnails) as part of the initial page load process to improve apparent page responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore a current research focus, as any byte savings will significantly enhance the experience of mobile device users. Toward this end, we propose a general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional LSTM recurrent networks. Our models address the main issues that have prevented autoencoder neural networks from competing with existing image compression algorithms: (1) our networks only need to be trained once (not per-image), regardless of input image dimensions and the desired compression rate; (2) our networks are progressive, meaning that the more bits are sent, the more accurate the image reconstruction; and (3) the proposed architecture is at least as efficient as a standard purpose-trained autoencoder for a given number of bits. On a large-scale benchmark of 32$\times$32 thumbnails, our LSTM-based approaches provide better visual quality than (headerless) JPEG, JPEG2000 and WebP, with a storage size that is reduced by 10% or more.

Connected through hyperedge HE-1 (induced by source paper "learning to structure an image with few colors"):
  with members:
    - "machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds"
    - "clustering with deep learning taxonomy and new methods"
    - "generative adversarial networks for extreme learned image compression"
    - "fast object detection in compressed video"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hybrid scene compression for visual localization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "discernible compressed images via deep perception consistency"):
  with members:
    - "conditional probability models for deep image compression"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"
    - "ssd single shot multibox detector"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (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"
    - "multiple description convolutional neural networks for 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"

Connected through hyperedge HE-5 (induced by source paper "approximating activation functions"):
  with members:
    - "searching for mobilenetv3"
    - "tensorflow a system for large scale machine learning"
    - "importance weighted autoencoders"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "intriguing properties of neural networks"
    - "generating sequences with recurrent neural networks"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "deep image compression using decoder side information"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "video compression through image interpolation"
    - "fast image processing with fully convolutional networks"
    - "learning binary residual representations for domain specific video streaming"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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-8 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"

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)