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: "agent modeling as auxiliary task for deep reinforcement learning"
  Abstract: In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent modeling: the first one based on parameter sharing, and the second one based on agent policy features. Both architectures aim to learn other agents' policies as auxiliary tasks, besides the standard actor (policy) and critic (values). We performed experiments in both cooperative and competitive domains. The former is a problem of coordinated multiagent object transportation and the latter is a two-player mini version of the Pommerman game. Our results show that the proposed architectures stabilize learning and outperform the standard A3C architecture when learning a best response in terms of expected rewards.

Connected through hyperedge HE-1 (induced by source paper "can increasing input dimensionality improve deep reinforcement learning"):
  with members:
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "decoupling dynamics and reward for transfer learning"
    - "sim to real transfer learning using robustified controllers in robotic tasks involving complex dynamics"
    - "self normalizing neural networks"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-2 (induced by source paper "rl agents implicitly learning human preferences"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "machine theory of mind"
    - "visualizing and understanding atari agents"
    - "graying the black box understanding dqns"

Connected through hyperedge HE-3 (induced by source paper "promoting coordination through policy regularization in multi agent deep reinforcement learning"):
  with members:
    - "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"
    - "learning to share and hide intentions using information regularization"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "variational autoencoders for opponent modeling in multi agent systems"):
  with members:
    - "dealing with non stationarity in multi agent deep reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "meta reinforcement learning of structured exploration strategies"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning policy representations in multiagent systems"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-5 (induced by source paper "accelerating training in pommerman with imitation and reinforcement learning"):
  with members:
    - "real time tree search with pessimistic scenarios"
    - "skynet a top deep rl agent in the inaugural pommerman team competition"
    - "safer deep rl with shallow mcts a case study in pommerman"
    - "continual match based training in pommerman technical report"
    - "backplay man muss immer umkehren"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "pommerman a multi agent playground"

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)