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 "improving 6d pose estimation of objects in clutter via physics aware monte carlo tree search"):
  Abstract: This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially in cases involving occlusions and objects resting on each other. The initial set of candidate object poses is generated from state-of-the-art object detection and global point cloud registration techniques. The best-scored pose per object by using these techniques may not be accurate due to overlaps and occlusions. Nevertheless, experimental indications provided in this work show that object poses with lower ranks may be closer to the real poses than ones with high ranks according to registration techniques. This motivates a global optimization process for improving these poses by taking into account scene-level physical interactions between objects. It also implies that the Cartesian product of candidate poses for interacting objects must be searched so as to identify the best scene-level hypothesis. To perform the search efficiently, the candidate poses for each object are clustered so as to reduce their number but still keep a sufficient diversity. Then, searching over the combinations of candidate object poses is performed through a Monte Carlo Tree Search (MCTS) process that uses the similarity between the observed depth image of the scene and a rendering of the scene given the hypothesized pose as a score that guides the search procedure. MCTS handles in a principled way the tradeoff between fine-tuning the most promising poses and exploring new ones, by using the Upper Confidence Bound (UCB) technique. Experimental results indicate that this process is able to quickly identify in cluttered scenes physically-consistent object poses that are significantly closer to ground truth compared to poses found by point cloud registration methods.

Includes members:
  Member "rgb d object detection and semantic segmentation for autonomous manipulation in clutter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "deep learning of local rgb d patches for 3d object detection and 6d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "3dmatch learning local geometric descriptors from rgb d reconstructions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scan2mesh from unstructured range scans to 3d meshes")
  Member "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects")
  Member "global hypothesis generation for 6d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "perch perception via search for multi object recognition and localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "verefine integrating object pose verification with iterative physics guided refinement")
  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)