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 "tossingbot learning to throw arbitrary objects with residual physics"):
  Abstract: We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. Videos are available at this https URL

Includes members:
  Member "residual policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "brpo batch residual policy optimization")
  Member "data augmented contact model for rigid body simulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling and analysis of non unique behaviors in multiple frictional impacts")
  Member "learning synergies between pushing and grasping with self supervised deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "residual reinforcement learning for robot control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning contact rich manipulation tasks with rigid position controlled robots learning to force control")
  Member "combining learned and analytical models for predicting action effects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video")
  Member "cartman the low cost cartesian manipulator that won the amazon robotics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pointins point based instance segmentation")
  Member "deep predictive policy training using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "let s push things forward a survey on robot pushing")

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)