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: "pearl preference appraisal reinforcement learning for motion planning"
  Abstract: Robot motion planning often requires finding trajectories that balance different user intents, or preferences. One of these preferences is usually arrival at the goal, while another might be obstacle avoidance. Here, we formalize these, and similar, tasks as preference balancing tasks (PBTs) on acceleration controlled robots, and propose a motion planning solution, PrEference Appraisal Reinforcement Learning (PEARL). PEARL uses reinforcement learning on a restricted training domain, combined with features engineered from user-given intents. PEARL's planner then generates trajectories in expanded domains for more complex problems. We present an adaptation for rejection of stochastic disturbances and offer in-depth analysis, including task completion conditions and behavior analysis when the conditions do not hold. PEARL is evaluated on five problems, two multi-agent obstacle avoidance tasks and three that stochastically disturb the system at run-time: 1) a multi-agent pursuit problem with 1000 pursuers, 2) robot navigation through 900 moving obstacles, which is is trained with in an environment with only 4 static obstacles, 3) aerial cargo delivery, 4) two robot rendezvous, and 5) flying inverted pendulum. Lastly, we evaluate the method on a physical quadrotor UAV robot with a suspended load influenced by a stochastic disturbance. The video, this https URL contains the experiments and visualization of the simulations.

Connected through hyperedge HE-1 (induced by source paper "multi agent motion planning for dense and dynamic environments via deep reinforcement learning"):
  with members:
    - "force based algorithm for motion planning of large agent teams"
    - "trajectory generation for multiagent point to point transitions via distributed model predictive control"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "downwash aware trajectory planning for large quadrotor teams"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"

Connected through hyperedge HE-2 (induced by source paper "long range indoor navigation with prm rl"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "deepnav learning to navigate large cities"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

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)