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: "barc backward reachability curriculum for robotic reinforcement learning"
  Abstract: Model-free Reinforcement Learning (RL) offers an attractive approach to learn control policies for high-dimensional systems, but its relatively poor sample complexity often forces training in simulated environments. Even in simulation, goal-directed tasks whose natural reward function is sparse remain intractable for state-of-the-art model-free algorithms for continuous control. The bottleneck in these tasks is the prohibitive amount of exploration required to obtain a learning signal from the initial state of the system. In this work, we leverage physical priors in the form of an approximate system dynamics model to design a curriculum scheme for a model-free policy optimization algorithm. Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance. BaRC is general, in that it can accelerate training of any model-free RL algorithm on a broad class of goal-directed continuous control MDPs. Its curriculum strategy is physically intuitive, easy-to-tune, and allows incorporating physical priors to accelerate training without hindering the performance, flexibility, and applicability of the model-free RL algorithm. We evaluate our approach on two representative dynamic robotic learning problems and find substantial performance improvement relative to previous curriculum generation techniques and naive exploration strategies.

Connected through hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey"):
  with members:
    - "language as a cognitive tool to imagine goals in curiosity driven exploration"
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "emergent tool use from multi agent autocurricula"
    - "distilling policy distillation"
    - "language grounding through social interactions and curiosity driven multi goal learning"
    - "automated curricula through setter solver interactions"
    - "continual learning for robotics"

Connected through hyperedge HE-2 (induced by source paper "competitive experience replay"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "backplay man muss immer umkehren"
    - "learning dexterous in hand manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "large scale study of curiosity driven learning"
    - "forward backward reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "evolving rewards to automate reinforcement learning"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning navigation behaviors end to end with autorl"
    - "evolution guided policy gradient in reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "learning to navigate the web"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "ttr based rewards for reinforcement learning with implicit model priors"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"
    - "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways"
    - "proximal policy optimization algorithms"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "multi vehicle collision avoidance via hamilton jacobi reachability and mixed integer programming"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "backplay man muss immer umkehren"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "pommerman a multi agent playground"
    - "kickstarting deep reinforcement learning"
    - "solving the rubik s cube without human knowledge"

Connected through hyperedge HE-6 (induced by source paper "learning navigation behaviors end to end with autorl"):
  with members:
    - "automated curriculum learning for neural networks"
    - "large scale evolution of image classifiers"
    - "reverse curriculum generation for reinforcement learning"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "learning to reinforcement learn"
    - "learning to learn by gradient descent by gradient descent"

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)