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: "increasing the action gap new operators for reinforcement learning"
  Abstract: This paper introduces new optimality-preserving operators on Q-functions. We first describe an operator for tabular representations, the consistent Bellman operator, which incorporates a notion of local policy consistency. We show that this local consistency leads to an increase in the action gap at each state; increasing this gap, we argue, mitigates the undesirable effects of approximation and estimation errors on the induced greedy policies. This operator can also be applied to discretized continuous space and time problems, and we provide empirical results evidencing superior performance in this context. Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator. As corollaries we provide a proof of optimality for Baird's advantage learning algorithm and derive other gap-increasing operators with interesting properties. We conclude with an empirical study on 60 Atari 2600 games illustrating the strong potential of these new operators.

Connected through hyperedge HE-1 (induced by source paper "maximizing the total reward via reward tweaking"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "on first order meta learning algorithms"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "on learning intrinsic rewards for policy gradient methods"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "a general framework for learning mean field games"):
  with members:
    - "q learning for mean field controls"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "a theory of regularized markov decision processes"
    - "multi agent reinforcement learning a report on challenges and approaches"
    - "real time bidding by reinforcement learning in display advertising"
    - "reinforcement learning with deep energy based policies"
    - "a unified view of entropy regularized markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "chainerrl a deep reinforcement learning library"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "catalyst rl a distributed framework for reproducible rl research"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "dopamine a research framework for deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-4 (induced by source paper "sample efficient model free reinforcement learning with off policy critics"):
  with members:
    - "exploration by random network distillation"
    - "dual policy iteration"
    - "information directed exploration for deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rudder return decomposition for delayed rewards"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "combining no regret and q learning"):
  with members:
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "stable predictive optimistic counterfactual regret minimization"
    - "learning to collaborate in markov decision processes"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"

Connected through hyperedge HE-6 (induced by source paper "algorithmic improvements for deep reinforcement learning applied to interactive fiction"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "textworld a learning environment for text based games"
    - "the text based adventure ai competition"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "towards solving text based games by producing adaptive action spaces"
    - "counting to explore and generalize in text based games"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "divide and conquer reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning with dynamic boltzmann softmax updates"):
  with members:
    - "meta gradient reinforcement learning"
    - "revisiting the softmax bellman operator new benefits and new perspective"
    - "learning to walk via deep reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "boltzmann exploration done right"
    - "reinforcement learning with deep energy based policies"
    - "pgq combining policy gradient and q learning"

Connected through hyperedge HE-8 (induced by source paper "on inductive biases in deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "investigating human priors for playing video games"
    - "distributed prioritized experience replay"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "the option critic architecture"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning values across many orders of magnitude"

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.AI(Artificial Intelligence)