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 "ttr based rewards for reinforcement learning with implicit model priors"):
  Abstract: Model-free reinforcement learning (RL) provides an attractive approach for learning control policies directly in high dimensional state spaces. However, many goal-oriented tasks involving sparse rewards remain difficult to solve with state-of-the-art model-free RL algorithms, even in simulation. One of the key difficulties is that deep RL, due to its relatively poor sample complexity, often requires a prohibitive number of trials to obtain a learning signal. We propose a novel, non-sparse reward function for robotic RL tasks by leveraging physical priors in the form of a time-to-reach (TTR) function computed from an approximate system dynamics model. TTR functions come from the optimal control field and measure the minimal time required to move from any state to the goal. However, TTR functions are intractable to compute for complex systems, so we compute it in a lower-dimensional state space, and then do a simple transformation to convert it into a TTR-based reward function for the MDP in RL tasks. Our TTR-based reward function provides highly-informative rewards that account for system dynamics.

Includes members:
  Member "barc backward reachability curriculum for robotic reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "feudal networks for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")
  Member "reverse curriculum generation for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling cyber physical human systems via an interplay between reinforcement learning and game theory")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "deep exploration via bootstrapped dqn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "exploration a study of count based exploration for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning curiosity algorithms")
  Member "multi vehicle collision avoidance via hamilton jacobi reachability and mixed integer programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a scalable framework for real time multi robot multi human collision avoidance")

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)