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: "challenges of real world reinforcement learning"
  Abstract: Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We present a set of nine unique challenges that must be addressed to productionize RL to real world problems. For each of these challenges, we specify the exact meaning of the challenge, present some approaches from the literature, and specify some metrics for evaluating that challenge. An approach that addresses all nine challenges would be applicable to a large number of real world problems. We also present an example domain that has been modified to present these challenges as a testbed for practical RL research.

Connected through hyperedge HE-1 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-2 (induced by source paper "an optimistic perspective on offline reinforcement learning"):
  with members:
    - "keep doing what worked behavioral modelling priors for offline reinforcement learning"
    - "benchmarking batch deep reinforcement learning algorithms"
    - "off policy policy gradient with state distribution correction"
    - "behavior regularized offline reinforcement learning"
    - "when to use parametric models in reinforcement learning"
    - "model based reinforcement learning for atari"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "a framework for data driven robotics"

Connected through hyperedge HE-3 (induced by source paper "how do we move learning to simulate with system dynamics"):
  with members:
    - "peeking into the future predicting future person activities and locations in videos"
    - "inferring metapopulation propagation network for intra city epidemic control and prevention"
    - "multi agent generative adversarial imitation learning"
    - "multi agent imitation learning for driving simulation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-4 (induced by source paper "the big three a methodology to increase data science roi by answering the questions companies care about"):
  with members:
    - "causal inference for social discrimination reasoning"
    - "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator"
    - "causal inference via kernel deviance measures"
    - "artificial intelligence paradigm for customer experience management in next generation networks challenges and perspectives"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "learning latent state representation for speeding up exploration"
    - "go explore a new approach for hard exploration problems"
    - "pyrep bringing v rep to deep robot learning"
    - "self supervised exploration via disagreement"
    - "provably efficient imitation learning from observation alone"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "pyrobot an open source robotics framework for research and benchmarking"

Connected through hyperedge HE-6 (induced by source paper "a framework for data driven robotics"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-7 (induced by source paper "recsim a configurable simulation platform for recommender systems"):
  with members:
    - "reinforcement learning when all actions are not always available"
    - "advantage amplification in slowly evolving latent state environments"
    - "toward simulating environments in reinforcement learning based recommendations"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning a deep listwise context model for ranking refinement"
    - "natural environment benchmarks for reinforcement learning"
    - "deep dyna q integrating planning for task completion dialogue policy learning"

Connected through hyperedge HE-8 (induced by source paper "caql continuous action q learning"):
  with members:
    - "equivalent and approximate transformations of deep neural networks"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "a lyapunov based approach to safe reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

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)