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: "multiagent evaluation under incomplete information"
  Abstract: This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents. Traditionally, researchers have relied on Elo ratings for this purpose, with recent works also using methods based on Nash equilibria. Unfortunately, Elo is unable to handle intransitive agent interactions, and other techniques are restricted to zero-sum, two-player settings or are limited by the fact that the Nash equilibrium is intractable to compute. Recently, a ranking method called {\alpha}-Rank, relying on a new graph-based game-theoretic solution concept, was shown to tractably apply to general games. However, evaluations based on Elo or {\alpha}-Rank typically assume noise-free game outcomes, despite the data often being collected from noisy simulations, making this assumption unrealistic in practice. This paper investigates multiagent evaluation in the incomplete information regime, involving general-sum many-player games with noisy outcomes. We derive sample complexity guarantees required to confidently rank agents in this setting. We propose adaptive algorithms for accurate ranking, provide correctness and sample complexity guarantees, then introduce a means of connecting uncertainties in noisy match outcomes to uncertainties in rankings. We evaluate the performance of these approaches in several domains, including Bernoulli games, a soccer meta-game, and Kuhn poker.

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"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent adversarial inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a generalized training approach for multiagent learning"):
  with members:
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "openspiel a framework for reinforcement learning in games"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "open ended learning in symmetric zero sum games"
    - "actor attention critic for multi agent reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"

Connected through hyperedge HE-3 (induced by source paper "alpha alpha rank practically scaling alpha rank through stochastic optimisation"):
  with members:
    - "a generalized training approach for multiagent learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "multi agent generalized recursive reasoning"
    - "openspiel a framework for reinforcement learning in games"
    - "bi level actor critic for multi agent coordination"
    - "open ended learning in symmetric zero sum games"
    - "maximum a posteriori policy optimisation"

Connected through hyperedge HE-4 (induced by source paper "learning probably approximately correct maximin strategies in simulation based games with infinite strategy spaces"):
  with members:
    - "competitive bridge bidding with deep neural networks"
    - "large scale learning of agent rationality in two player zero sum games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "a generalised method for empirical game theoretic analysis"
    - "bandit learning in concave n person games"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "safe and nested subgame solving for imperfect information games"
    - "on kernelized multi armed bandits"

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.MA(Multiagent Systems)