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: "forward backward reinforcement learning"
  Abstract: Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. While much of the power of reinforcement learning derives from the concept that agents can learn with little guidance, this requirement greatly burdens the training process. If we relax this one restriction and endow the agent with knowledge of the reward function, and in particular of the goal, we can leverage backwards induction to accelerate training. To achieve this, we propose training a model to learn to take imagined reversal steps from known goal states. Rather than training an agent exclusively to determine how to reach a goal while moving forwards in time, our approach travels backwards to jointly predict how we got there. We evaluate our work in Gridworld and Towers of Hanoi and empirically demonstrate that it yields better performance than standard DDQN.

Connected through hyperedge HE-1 (induced by source paper "empirical policy evaluation with supergraphs"):
  with members:
    - "recall traces backtracking models for efficient reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"
    - "on the role of clustering in personalized pagerank estimation"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "fast bidirectional probability estimation in markov models"
    - "personalized pagerank to a target node"

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"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "learning compositional neural programs with recursive tree search and planning"):
  with members:
    - "learning to infer program sketches"
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "hierarchical reinforcement learning with hindsight"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "improving the universality and learnability of neural programmer interpreters with combinator abstraction"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-4 (induced by source paper "addressing sample complexity in visual tasks using her and hallucinatory gans"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "imitating latent policies from observation"
    - "visual reinforcement learning with imagined goals"
    - "world models"
    - "few shot goal inference for visuomotor learning and planning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"

Connected through hyperedge HE-5 (induced by source paper "generative predecessor models for sample efficient imitation learning"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "time contrastive networks self supervised learning from video"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "visual hindsight experience replay"):
  with members:
    - "imitating latent policies from observation"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"
    - "world models"
    - "few shot goal inference for visuomotor learning and planning"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "asymmetric actor critic for image based robot learning"

Connected through hyperedge HE-7 (induced by source paper "rudder return decomposition for delayed rewards"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "meta gradient reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "world models"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "methods for interpreting and understanding deep neural networks"

Connected through hyperedge HE-8 (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"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "pommerman a multi agent playground"
    - "kickstarting deep reinforcement learning"
    - "solving the rubik s cube without human knowledge"

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.LG(Machine Learning)