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 with opponent learning awareness"
  Abstract: Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimisation. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results. We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes a term that accounts for the impact of one agent's policy on the anticipated parameter update of the other agents. Results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners' dilemma, while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods. Applied to repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL. The method thus scales to large parameter and input spaces and nonlinear function approximators. We apply LOLA to a grid world task with an embedded social dilemma using recurrent policies and opponent modelling. By explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest. The code is at this http URL.

Connected through hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

Connected through hyperedge HE-2 (induced by source paper "algorithms in multi agent systems a holistic perspective from reinforcement learning and game theory"):
  with members:
    - "single deep counterfactual regret minimization"
    - "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"
    - "bounds for approximate regret matching algorithms"
    - "combining no regret and q learning"
    - "a generalized training approach for multiagent learning"
    - "stable predictive optimistic counterfactual regret minimization"
    - "multiagent evaluation under incomplete information"
    - "a survey and critique of multiagent deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "intrinsic motivation for encouraging synergistic behavior"):
  with members:
    - "learning gentle object manipulation with curiosity driven deep reinforcement learning"
    - "self supervised exploration via disagreement"
    - "efficient bimanual manipulation using learned task schemas"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "emergent coordination through competition"
    - "large scale study of curiosity driven learning"
    - "emergence of structured behaviors from curiosity based intrinsic motivation"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-4 (induced by source paper "learning to resolve alliance dilemmas in many player zero sum games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "learning reciprocity in complex sequential social dilemmas"
    - "emergent coordination through competition"
    - "no press diplomacy modeling multi agent gameplay"
    - "open ended learning in symmetric zero sum games"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "on gradient based learning in continuous games"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "bandit learning in concave n person games"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "a generic approach for escaping saddle points"
    - "cycles in adversarial regularized learning"
    - "improved training of wasserstein gans"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-6 (induced by source paper "emergence of pragmatics from referential game between theory of mind agents"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "planning inference and pragmatics in sequential language games"
    - "emergence of communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication through negotiation"
    - "emergent communication in a multi modal multi step referential game"

Connected through hyperedge HE-7 (induced by source paper "silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "legible normativity for ai alignment the value of silly rules"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "evolving intrinsic motivations for altruistic behavior"
    - "representation learning with contrastive predictive coding"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

Connected through hyperedge HE-8 (induced by source paper "real world human robot collaborative reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "human visual attention prediction boosts learning performance of autonomous driving agents"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "pytorch an imperative style high performance deep learning library"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shared autonomy via deep reinforcement learning"
    - "machine theory of mind"

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)