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: "learning to place new objects"
  Abstract: The ability to place objects in the environment is an important skill for a personal robot. An object should not only be placed stably, but should also be placed in its preferred location/orientation. For instance, a plate is preferred to be inserted vertically into the slot of a dish-rack as compared to be placed horizontally in it. Unstructured environments such as homes have a large variety of object types as well as of placing areas. Therefore our algorithms should be able to handle placing new object types and new placing areas. These reasons make placing a challenging manipulation task. In this work, we propose a supervised learning algorithm for finding good placements given the point-clouds of the object and the placing area. It learns to combine the features that capture support, stability and preferred placements using a shared sparsity structure in the parameters. Even when neither the object nor the placing area is seen previously in the training set, our algorithm predicts good placements. In extensive experiments, our method enables the robot to stably place several new objects in several new placing areas with 98% success-rate; and it placed the objects in their preferred placements in 92% of the cases.

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"
    - "multi view picking next best view reaching for improved grasping in clutter"

Connected through hyperedge HE-2 (induced by source paper "learning 6 dof grasping and pick place using attention focus"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "hindsight experience replay"
    - "pick and place without geometric object models"
    - "grasp pose detection in point clouds"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "continuous control with deep reinforcement learning"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

Connected through hyperedge HE-3 (induced by source paper "learning object arrangements in 3d scenes using human context"):
  with members:
    - "learning to place new objects in a scene"
    - "towards holistic scene understanding feedback enabled cascaded classification models"

Connected through hyperedge HE-4 (induced by source paper "learning to place new objects in a scene"):
  with members:
    - "towards holistic scene understanding feedback enabled cascaded classification models"

Connected through hyperedge HE-5 (induced by source paper "unstructured human activity detection from rgbd images"):
  with members:
    - "learning to place new objects in a scene"

Connected through hyperedge HE-6 (induced by source paper "labeling 3d scenes for personal assistant robots"):
  with members:
    - "towards holistic scene understanding feedback enabled cascaded classification models"

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)