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: "harmonic networks integrating spectral information into cnns"
  Abstract: Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In contrast, in this paper we propose harmonic blocks that produce features by learning optimal combinations of spectral filters defined by the Discrete Cosine Transform. The harmonic blocks are used to replace conventional convolutional layers to construct partial or fully harmonic CNNs. We extensively validate our approach and show that the introduction of harmonic blocks into state-of-the-art CNN baseline architectures results in comparable or better performance in classification tasks on small NORB, CIFAR10 and CIFAR100 datasets.

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"
    - "deep residual learning in the jpeg transform domain"
    - "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"
    - "deep residual learning in the jpeg transform domain"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "harmonic networks with limited training samples"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "gabor convolutional networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"
    - "a multi branch convolutional neural network for detecting double jpeg compression"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "wavelet convolutional neural networks for texture classification"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-4 (induced by source paper "hybrid cosine based convolutional neural networks"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "pyramid scene parsing network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "compressing neural networks with the hashing trick"
    - "very deep convolutional networks for large scale image recognition"
    - "speeding up convolutional neural networks with low rank expansions"

Connected through hyperedge HE-5 (induced by source paper "deep residual learning in the jpeg transform domain"):
  with members:
    - "compressed video action recognition"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"

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)