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: "learning to share and hide intentions using information regularization"
  Abstract: Learning to cooperate with friends and compete with foes is a key component of multi-agent reinforcement learning. Typically to do so, one requires access to either a model of or interaction with the other agent(s). Here we show how to learn effective strategies for cooperation and competition in an asymmetric information game with no such model or interaction. Our approach is to encourage an agent to reveal or hide their intentions using an information-theoretic regularizer. We consider both the mutual information between goal and action given state, as well as the mutual information between goal and state. We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning. Finally, we demonstrate that cooperative (competitive) policies learned with our approach lead to more (less) reward for a second agent in two simple asymmetric information games.

Connected through hyperedge HE-1 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "kickstarting deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "influence based multi agent exploration"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "coordinated exploration via intrinsic rewards for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "exploration by random network distillation"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "information maximizing exploration with a latent dynamics model"

Connected through hyperedge HE-3 (induced by source paper "preventing imitation learning with adversarial policy ensembles"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adversarial policies attacking deep reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "policy poisoning in batch reinforcement learning and control"
    - "learning novel policies for tasks"
    - "causal confusion in imitation learning"
    - "risk aware active inverse reinforcement learning"
    - "interpretable latent spaces for learning from demonstration"

Connected through hyperedge HE-4 (induced by source paper "promoting coordination through policy regularization in multi agent deep reinforcement learning"):
  with members:
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "maximum entropy diverse exploration disentangling maximum entropy reinforcement learning"):
  with members:
    - "diverse exploration via conjugate policies for policy gradient methods"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "diversity driven exploration strategy for deep reinforcement learning"
    - "stein variational policy gradient"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-6 (induced by source paper "finding friend and foe in multi agent games"):
  with members:
    - "leveling the playing field fairness in ai versus human game benchmarks"
    - "theory of minds understanding behavior in groups through inverse planning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

Connected through hyperedge HE-7 (induced by source paper "ds vic unsupervised discovery of decision states for transfer in rl"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "exploration by random network distillation"
    - "diversity is all you need learning skills without a reward function"
    - "variational option discovery algorithms"
    - "curiosity driven exploration by self supervised prediction"
    - "distral robust multitask reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "deep variational information bottleneck"

Connected through hyperedge HE-8 (induced by source paper "unsupervised discovery of decision states for transfer in reinforcement learning"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "exploration by random network distillation"
    - "diversity is all you need learning skills without a reward function"
    - "variational option discovery algorithms"
    - "curiosity driven exploration by self supervised prediction"
    - "distral robust multitask reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "deep variational information bottleneck"

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)