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: "doorgym a scalable door opening environment and baseline agent"
  Abstract: Reinforcement Learning (RL) has brought forth ideas of autonomous robots that can navigate real-world environments with ease, aiding humans in a variety of tasks. RL agents have just begun to make their way out of simulation into the real world. Once in the real world, benchmark tasks often fail to transfer into useful skills. We introduce DoorGym, a simulation environment intended to be the first step to move RL from toy environments towards useful atomic skills that can be composed and extended towards a broader goal. DoorGym is an open-source door simulation framework designed to be highly configurable. We also provide a baseline PPO (Proximal Policy Optimization) and SAC (Soft Actor-Critic)implementation, which achieves a success rate of up to 70% for common tasks in this environment. Environment kit available here:this https URL

Connected through hyperedge HE-1 (induced by source paper "ikea furniture assembly environment for long horizon complex manipulation tasks"):
  with members:
    - "to follow or not to follow selective imitation learning from observations"
    - "monet unsupervised scene decomposition and representation"
    - "rlbench the robot learning benchmark learning environment"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "deepmind control suite"
    - "virtualhome simulating household activities via programs"
    - "learning dexterous in hand manipulation"
    - "unity a general platform for intelligent agents"

Connected through hyperedge HE-2 (induced by source paper "interactive gibson benchmark for interactive navigation in cluttered environments"):
  with members:
    - "vrkitchen an interactive 3d virtual environment for task oriented learning"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "deep visual mpc policy learning for navigation"
    - "habitat a platform for embodied ai research"
    - "gibson env real world perception for embodied agents"
    - "unity a general platform for intelligent agents"
    - "on evaluation of embodied navigation agents"
    - "soft actor critic algorithms and applications"

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)