human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi view picking next best view reaching for improved grasping in clutter"
  Abstract: Camera viewpoint selection is an important aspect of visual grasp detection, especially in clutter where many occlusions are present. Where other approaches use a static camera position or fixed data collection routines, our Multi-View Picking (MVP) controller uses an active perception approach to choose informative viewpoints based directly on a distribution of grasp pose estimates in real time, reducing uncertainty in the grasp poses caused by clutter and occlusions. In trials of grasping 20 objects from clutter, our MVP controller achieves 80% grasp success, outperforming a single-viewpoint grasp detector by 12%. We also show that our approach is both more accurate and more efficient than approaches which consider multiple fixed viewpoints.

Connected through hyperedge HE-1 (induced by source paper "learning manipulation skills via hierarchical spatial attention"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "few shot goal inference for visuomotor learning and planning"
    - "deictic image maps an abstraction for learning pose invariant manipulation policies"
    - "hindsight experience replay"

Connected through hyperedge HE-2 (induced by source paper "egad an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation"):
  with members:
    - "pointnetgpd detecting grasp configurations from point sets"
    - "towards the targeted environment specific evolution of robot components"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "diversity is all you need learning skills without a reward function"
    - "densely supervised grasp detector dsgd"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "grasp pose detection in point clouds"

Question: Please tell me which class the center node 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)