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: "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
  Abstract: Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. These are critical shortcomings for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction. Thus, we develop a novel class of off-policy batch RL algorithms, which are able to effectively learn offline, without exploring, from a fixed batch of human interaction data. We leverage models pre-trained on data as a strong prior, and use KL-control to penalize divergence from this prior during RL training. We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning. The algorithms are tested on the problem of open-domain dialog generation -- a challenging reinforcement learning problem with a 20,000-dimensional action space. Using our Way Off-Policy algorithm, we can extract multiple different reward functions post-hoc from collected human interaction data, and learn effectively from all of these. We test the real-world generalization of these systems by deploying them live to converse with humans in an open-domain setting, and demonstrate that our algorithm achieves significant improvements over prior methods in off-policy batch RL.

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"
    - "challenges of real world reinforcement learning"
    - "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"
    - "challenges of real world reinforcement learning"
    - "model based reinforcement learning for atari"
    - "a framework for data driven robotics"

Connected through hyperedge HE-3 (induced by source paper "brpo batch residual policy optimization"):
  with members:
    - "benchmarking batch deep reinforcement learning algorithms"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "residual policy learning"
    - "natural gradient deep q learning"
    - "off policy deep reinforcement learning without exploration"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "residual reinforcement learning for robot control"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "hierarchical reinforcement learning for open domain dialog"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "evaluating coherence in dialogue systems using entailment"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "team qcri mit at semeval 2019 task 4 propaganda analysis meets hyperpartisan news detection"

Connected through hyperedge HE-5 (induced by source paper "fine tuning language models from human preferences"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "finding generalizable evidence by learning to convince q a models"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "towards coherent and cohesive long form text generation"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "preference based interactive multi document summarisation"

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"
    - "challenges of real world reinforcement learning"
    - "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 "iris implicit reinforcement without interaction at scale for learning control from offline robot manipulation data"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "towards characterizing divergence in deep q learning"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "learning latent plans from play"
    - "goal conditioned imitation learning"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"

Connected through hyperedge HE-8 (induced by source paper "bail best action imitation learning for batch deep reinforcement learning"):
  with members:
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "off policy evaluation via off policy classification"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"

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)