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 "video enhancement with task oriented flow"):
  Abstract: Many video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a sub-optimal representation for particular video processing tasks. In this paper, we propose task-oriented flow (TOFlow), a motion representation learned in a self-supervised, task-specific manner. We design a neural network with a trainable motion estimation component and a video processing component, and train them jointly to learn the task-oriented flow. For evaluation, we build Vimeo-90K, a large-scale, high-quality video dataset for low-level video processing. TOFlow outperforms traditional optical flow on standard benchmarks as well as our Vimeo-90K dataset in three video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution.

Includes members:
  Member "frame recurrent video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "restore from restored video restoration with pseudo clean video")
  Member "to learn image super resolution use a gan to learn how to do image degradation first":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "fast accurate and lightweight super resolution with cascading residual network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "multi frame quality enhancement for compressed video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a foreground background parallel compression with residual encoding for surveillance video")
  Member "context aware synthesis for video frame interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "softmax splatting for video frame interpolation")
  Member "flow guided feature aggregation for video object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "super slomo high quality estimation of multiple intermediate frames for video interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic latent residual video prediction")
  Member "accurate optical flow via direct cost volume processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising")

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)