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 "self supervised object in gripper segmentation from robotic motions"):
  Abstract: We present a novel technique to automatically generate annotated data for important robotic perception tasks such as object segmentation and 3D object reconstruction using a robot manipulator. Our self-supervised method can segment unknown objects from a robotic gripper in RGB video sequences by exploiting motion and temporal cues. The key aspect of our approach in contrast to existing systems is its independence of any hardware specifics such as extrinsic and intrinsic camera calibration and a robot model. We achieve this using a two-step process: First, we learn to predict segmentation masks for our given manipulator using optical flow estimation. Then, these masks are used in combination with motion cues to automatically distinguish between the manipulator, the background, and the unknown, grasped object. We perform a thorough comparison with alternative baselines and approaches in the literature. The obtained object views and masks are suitable training data for segmentation networks that generalize to novel environments and also allow for watertight 3D object reconstruction.

Includes members:
  Member "self supervised model adaptation for multimodal semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "towards segmenting everything that moves":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "photorealistic image synthesis for object instance detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "template based unseen instance detection")
  Member "deep neural network concepts for background subtraction a systematic review and comparative evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "learning instance segmentation by interaction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object discovery with a copy pasting gan")
  Member "deep visual domain adaptation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "object discovery in videos as foreground motion clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiple object tracking by flowing and fusing")

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)