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: "can deep reinforcement learning solve erdos selfridge spencer games"
  Abstract: Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and correspondingly diagnose individual actions against such a characterization. Here we consider a family of combinatorial games, arising from work of Erdos, Selfridge, and Spencer, and we propose their use as environments for evaluating and comparing different approaches to reinforcement learning. These games have a number of appealing features: they are challenging for current learning approaches, but they form (i) a low-dimensional, simply parametrized environment where (ii) there is a linear closed form solution for optimal behavior from any state, and (iii) the difficulty of the game can be tuned by changing environment parameters in an interpretable way. We use these Erdos-Selfridge-Spencer games not only to compare different algorithms, but test for generalization, make comparisons to supervised learning, analyse multiagent play, and even develop a self play algorithm. Code can be found at: this https URL

Connected through hyperedge HE-1 (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-2 (induced by source paper "toybox a suite of environments for experimental evaluation of deep reinforcement learning"):
  with members:
    - "let s play again variability of deep reinforcement learning agents in atari environments"
    - "modularization of end to end learning case study in arcade games"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "exploration by random network distillation"
    - "strategic object oriented reinforcement learning"
    - "natural environment benchmarks for reinforcement learning"
    - "investigating human priors for playing video games"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "off policy evaluation via off policy classification"):
  with members:
    - "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"
    - "stochastic adversarial video prediction"
    - "representation balancing mdps for off policy policy evaluation"
    - "quantifying generalization in reinforcement learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "rudder return decomposition for delayed rewards"
    - "gotta learn fast a new benchmark for generalization in rl"

Connected through hyperedge HE-4 (induced by source paper "interaction aware multi agent reinforcement learning for mobile agents with individual goals"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "towards cooperation in sequential prisoner s dilemmas a deep multiagent reinforcement learning approach"
    - "counterfactual multi agent policy gradients"
    - "emergent complexity via multi agent competition"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-5 (induced by source paper "toybox better atari environments for testing reinforcement learning agents"):
  with members:
    - "let s play again variability of deep reinforcement learning agents in atari environments"
    - "game ai research with fast planet wars variants"
    - "terrain rl simulator"
    - "distributed evaluations ending neural point metrics"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "deep reinforcement learning a brief survey"
    - "starcraft ii a new challenge for reinforcement learning"
    - "control of a quadrotor with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a study on overfitting in deep reinforcement learning"):
  with members:
    - "adversarial attacks on neural network policies"
    - "deep reinforcement learning that matters"
    - "fisher rao metric geometry and complexity of neural networks"
    - "starcraft ii a new challenge for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "iroko a framework to prototype reinforcement learning for data center traffic control"):
  with members:
    - "deep reinforcement learning that matters"
    - "rllib abstractions for distributed reinforcement learning"
    - "proximal policy optimization algorithms"
    - "medicine rapid prototyping of production ready network services in multi pop environments"
    - "benchmarking deep reinforcement learning for continuous control"
    - "prioritized experience replay"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "keep it stupid simple"):
  with members:
    - "ai safety gridworlds"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "imagination augmented agents for deep reinforcement learning"

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)