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 "effects of a social force model reward in robot navigation based on deep reinforcement learning"):
  Abstract: In this paper is proposed an inclusion of the Social Force Model (SFM) into a concrete Deep Reinforcement Learning (RL) framework for robot navigation. These types of techniques have demonstrated to be useful to deal with different types of environments to achieve a goal. In Deep RL, a description of the world to describe the states and a reward adapted to the environment are crucial elements to get the desire behaviour and achieve a high performance. For this reason, this work adds a dense reward function based on SFM and uses the forces in the states like an additional description. Furthermore, obstacles are added to improve the behaviour of works that only consider moving agents. This SFM inclusion can offer a better description of the obstacles for the navigation. Several simulations have been done to check the effects of these modifications in the average performance.

Includes members:
  Member "situation aware pedestrian trajectory prediction with spatio temporal attention model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle")
  Member "long range indoor navigation with prm rl":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation")
  Member "reward shaping via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "deep visual mpc policy learning for navigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "badgr an autonomous self supervised learning based navigation system")
  Member "sophie an attentive gan for predicting paths compliant to social and physical constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformer networks for trajectory forecasting")
  Member "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to move with affordance maps")
  Member "social gan socially acceptable trajectories with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")

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)