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: "towards image understanding from deep compression without decoding"
  Abstract: Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced by these compression methods. Since the encoders and decoders in DNN-based compression methods are neural networks with feature-maps as internal representations of the images, we directly integrate these with architectures for image understanding. This bypasses decoding of the compressed representation into RGB space and reduces computational cost. Our study shows that accuracies comparable to networks that operate on compressed RGB images can be achieved while reducing the computational complexity up to $2\times$. Furthermore, we show that synergies are obtained by jointly training compression networks with classification networks on the compressed representations, improving image quality, classification accuracy, and segmentation performance. We find that inference from compressed representations is particularly advantageous compared to inference from compressed RGB images for aggressive compression rates.

Connected through hyperedge HE-1 (induced by source paper "towards coding for human and machine vision a scalable image coding approach"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"

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 "progressive compressed records taking a byte out of deep learning data"):
  with members:
    - "dataset distillation"
    - "discrepancy coresets and sketches in machine learning"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-4 (induced by source paper "online learned continual compression with stacked quantization modules"):
  with members:
    - "decoupled greedy learning of cnns"
    - "generating diverse high fidelity images with vq vae 2"
    - "unsupervised state representation learning in atari"
    - "continual learning with tiny episodic memories"
    - "compressing the input for cnns with the first order scattering transform"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "deep generative modeling of lidar data"

Connected through hyperedge HE-5 (induced by source paper "fast object detection in compressed jpeg images"):
  with members:
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "fast object detection in compressed video"
    - "yolov3 an incremental improvement"
    - "deep residual learning in the jpeg transform domain"
    - "harmonic networks integrating spectral information into cnns"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "fssd feature fusion single shot multibox detector"
    - "compressed video action recognition"

Connected through hyperedge HE-6 (induced by source paper "adacompress adaptive compression for online computer vision services"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepn jpeg a deep neural network favorable jpeg based image compression framework"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "robust physical world attacks on deep learning models"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "full resolution image compression with recurrent neural networks"
    - "understanding how image quality affects deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "exploring semantic segmentation on the dct representation"):
  with members:
    - "harmonic networks with limited training samples"
    - "fast object detection in compressed jpeg images"
    - "dct domain deep convolutional neural networks for multiple jpeg compression classification"
    - "deep residual learning in the jpeg transform domain"
    - "harmonic networks integrating spectral information into cnns"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "compressed video action recognition"
    - "a multi branch convolutional neural network for detecting double jpeg compression"

Connected through hyperedge HE-8 (induced by source paper "deep joint source channel coding for wireless image retrieval"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep joint source channel coding for wireless image transmission"
    - "horizontal pyramid matching for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

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)