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: "deep residual learning in the jpeg transform domain"
  Abstract: We introduce a general method of performing Residual Network inference and learning in the JPEG transform domain that allows the network to consume compressed images as input. Our formulation leverages the linearity of the JPEG transform to redefine convolution and batch normalization with a tune-able numerical approximation for ReLu. The result is mathematically equivalent to the spatial domain network up to the ReLu approximation accuracy. A formulation for image classification and a model conversion algorithm for spatial domain networks are given as examples of the method. We show that the sparsity of the JPEG format allows for faster processing of images with little to no penalty in the network accuracy.

Connected through hyperedge HE-1 (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"
    - "harmonic networks integrating spectral information into cnns"
    - "towards image understanding from deep compression without decoding"
    - "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-2 (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"
    - "harmonic networks integrating spectral information into cnns"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "towards image understanding from deep compression without decoding"
    - "compressed video action recognition"
    - "a multi branch convolutional neural network for detecting double jpeg 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.LG(Machine Learning)