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 "learning end to end scene flow by distilling single tasks knowledge"):
  Abstract: Scene flow is a challenging task aimed at jointly estimating the 3D structure and motion of the sensed environment. Although deep learning solutions achieve outstanding performance in terms of accuracy, these approaches divide the whole problem into standalone tasks (stereo and optical flow) addressing them with independent networks. Such a strategy dramatically increases the complexity of the training procedure and requires power-hungry GPUs to infer scene flow barely at 1 FPS. Conversely, we propose DWARF, a novel and lightweight architecture able to infer full scene flow jointly reasoning about depth and optical flow easily and elegantly trainable end-to-end from scratch. Moreover, since ground truth images for full scene flow are scarce, we propose to leverage on the knowledge learned by networks specialized in stereo or flow, for which much more data are available, to distill proxy annotations. Exhaustive experiments show that i) DWARF runs at about 10 FPS on a single high-end GPU and about 1 FPS on NVIDIA Jetson TX2 embedded at KITTI resolution, with moderate drop in accuracy compared to 10x deeper models, ii) learning from many distilled samples is more effective than from the few, annotated ones available. Code available at: this https URL

Includes members:
  Member "learning to adapt for stereo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain invariant stereo matching networks")
  Member "sense a shared encoder network for scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the five elements of flow")
  Member "guided stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "real time semantic stereo matching")
  Member "ga net guided aggregation net for end to end stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "pwoc 3d deep occlusion aware end to end scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation")
  Member "ddflow learning optical flow with unlabeled data distillation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization")
  Member "real time semantic stereo matching":
    also connected through other hyperedges:
      (none)
  Member "deep rigid instance scene flow":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation")

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)