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 "learning arbitration for shared autonomy by hindsight data aggregation"):
  Abstract: In this paper we present a framework for the teleoperation of pick-and-place tasks. We define a shared control policy that allows to blend between direct user control and autonomous control based on user intent inference. One of the main challenges in shared autonomy systems is to define the arbitration function, which decides when to let the autonomous agent take over. In this work, we propose a model and training method to learn the arbitration function. Our model is based on a recurrent neural network that takes as input the state, intent prediction scores and user command to produce an arbitration between user and robot commands. This work extends our previous work on differentiable policies for shared autonomy. Differentiability of the policy is desirable to further train the shared autonomy system end-to-end. In this work we propose training of the arbitration function by using data from user performing the task with shared control. We present initial results by teleoperating a gripper in a virtual environment using pre-trained motion generation and intent prediction. We compare our data aggregation training procedure to a handcrafted arbitration function. Our preliminary results show the efficacy of the approach and shed light on limitations that we believe demonstrate the need for user adaptability in shared autonomy systems.

Includes members:
  Member "a blended human robot shared control framework to handle drift and latency":
    also connected through other hyperedges:
      (none)
  Member "shared autonomy via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real world human robot collaborative reinforcement learning")
  Member "shared autonomy via hindsight optimization for teleoperation and teaming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling")
  Member "real time perception meets reactive motion generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "6 pack category level 6d pose tracker with anchor based keypoints")
  Member "human robot mutual adaptation in shared autonomy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a general arbitration model for robust human robot shared control with multi source uncertainty modeling")
  Member "goal set inverse optimal control and iterative re planning for predicting human reaching motions in shared workspaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "desire distant future prediction in dynamic scenes with interacting agents")

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)