human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "exploring semantic segmentation on the dct representation"):
  Abstract: Typical convolutional networks are trained and conducted on RGB images. However, images are often compressed for memory savings and efficient transmission in real-world applications. In this paper, we explore methods for performing semantic segmentation on the discrete cosine transform (DCT) representation defined by the JPEG standard. We first rearrange the DCT coefficients to form a preferred input type, then we tailor an existing network to the DCT inputs. The proposed method has an accuracy close to the RGB model at about the same network complexity. Moreover, we investigate the impact of selecting different DCT components on segmentation performance. With a proper selection, one can achieve the same level accuracy using only 36% of the DCT coefficients. We further show the robustness of our method under quantization errors. To our knowledge, this paper is the first to explore semantic segmentation on the DCT representation.

Includes members:
  Member "harmonic networks with limited training samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scale equivariant steerable networks")
  Member "fast object detection in compressed jpeg images":
    also connected through other hyperedges:
      (none)
  Member "dct domain deep convolutional neural networks for multiple jpeg compression classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi patch aggregation models for resampling detection")
  Member "deep residual learning in the jpeg transform domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast object detection in compressed jpeg images")
  Member "harmonic networks integrating spectral information into cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast object detection in compressed jpeg images")
  Member "efficient dense modules of asymmetric convolution for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatial sampling network for fast scene understanding")
  Member "towards image understanding from deep compression without decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards coding for human and machine vision a scalable image coding approach")
  Member "compressed video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "motion excited sampler video adversarial attack with sparked prior")

Question: Please tell me which class the source paper 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)