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 "motion based object segmentation based on dense rgb d scene flow"):
  Abstract: Given two consecutive RGB-D images, we propose a model that estimates a dense three-dimensional (3D) motion field, also known as  scene flow . We take advantage of the fact that in robot manipulation scenarios, scenes often consist of a set of rigidly moving objects. Our model jointly estimates the following: First, the segmentation of the scene into an unknown but finite number of objects, second, the motion trajectories of these objects, and finally, the object scene flow. We employ an hourglass, deep neural network architecture. In the encoding stage, the RGB and depth images undergo spatial compression and correlation. In the decoding stage, the model outputs three images containing a per-pixel estimate of the corresponding object center as well as object translation and rotation. This forms the basis for inferring the object segmentation and final object scene flow. To evaluate our model, we generated a new and challenging, large scale, synthetic dataset that is specifically targeted at robotic manipulation: It contains a large number of scenes with a very diverse set of simultaneously moving 3D objects and is recorded with a simulated, static RGB-D camera. In quantitative experiments, we show that we outperform state-of-the-art scene flow and motion-segmentation methods on this data set. In qualitative experiments, we show how our learned model transfers to challenging real-world scenes, visually generating better results than existing methods.

Includes members:
  Member "interactive perception leveraging action in perception and perception in action":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action for better prediction")
  Member "higher order minimum cost lifted multicuts for motion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object segmentation tracking from generic video cues")
  Member "flownet 2 0 evolution of optical flow estimation with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "scene flow estimation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "driving datasets literature review")
  Member "shapenet an information rich 3d model repository":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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.RO(Robotics)