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 "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"):
  Abstract: Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many distractors, often in restricted spaces. To tackle these challenges, we developed a deep-learning approach that combines object detection and semantic segmentation. The manipulation scenes are captured with RGB-D cameras, for which we developed a depth fusion method. Employing pretrained features makes learning from small annotated robotic datasets possible. We evaluate our approach on two challenging datasets: one captured for the Amazon Picking Challenge 2016, where our team NimbRo came in second in the Stowing and third in the Picking task; and one captured in disaster-response scenarios. The experiments show that object detection and semantic segmentation complement each other and can be combined to yield reliable object perception.

Includes members:
  Member "nimbro rescue solving disaster response tasks through mobile manipulation robot momaro":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "remote mobile manipulation with the centauro robot full body telepresence and autonomous operator assistance")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "refinenet multi path refinement networks for high resolution semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "a summary of team mit s approach to the amazon picking challenge 2015":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "development of a robotic system for automated decaking of 3d printed parts")
  Member "iterative visual recognition for learning based randomized bin picking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "experiments on learning based industrial bin picking with iterative visual recognition")
  Member "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "team delft s robot winner of the amazon picking challenge 2016":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects")

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)