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: "a deep policy inference q network for multi agent systems"
  Abstract: We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems---modeling agents with varying strategies---and propose to employ "policy features" learned from raw observations (e.g., raw images) of collaborators and opponents by inferring their policies. DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learning models. We further propose an enhanced version of DPIQN, called deep recurrent policy inference Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN are trained by an adaptive training procedure, which adjusts the network's attention to learn the policy features and its own Q-values at different phases of the training process. We present a comprehensive analysis of DPIQN and DRPIQN, and highlight their effectiveness and generalizability in various multi-agent settings. Our models are evaluated in a classic soccer game involving both competitive and collaborative scenarios. Experimental results performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate superior performance to the baseline DQN and deep recurrent Q-network (DRQN) models. We also explore scenarios in which collaborators or opponents dynamically change their policies, and show that DPIQN and DRPIQN do lead to better overall performance in terms of stability and mean scores.

Connected through hyperedge HE-1 (induced by source paper "agent modeling as auxiliary task for deep reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "deep reinforcement learning for general video game ai"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "lenient multi agent deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies"):
  with members:
    - "leveraging statistical multi agent online planning with emergent value function approximation"
    - "open loop execution of tree search algorithms"
    - "machine theory of mind"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "feedback based tree search for reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning fairness in multi agent systems"):
  with members:
    - "a cooperative multi agent reinforcement learning framework for resource balancing in complex logistics network"
    - "the matthew effect in computation contests high difficulty may lead to 51 dominance"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "evolving intrinsic motivations for altruistic behavior"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "machine theory of mind"

Connected through hyperedge HE-4 (induced by source paper "mapel multi agent pursuer evader learning using situation report"):
  with members:
    - "local wealth redistribution promotes cooperation in multiagent systems"
    - "counterfactual multi agent policy gradients"
    - "lenient multi agent deep reinforcement learning"
    - "learning with opponent learning awareness"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "opponent modeling in deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "robust temporal difference learning for critical domains"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "multi step reinforcement learning a unifying algorithm"
    - "value decomposition networks for cooperative multi agent learning"
    - "the option critic architecture"
    - "safe and efficient off policy reinforcement learning"
    - "multidefender security games"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-6 (induced by source paper "tactical reward shaping bypassing reinforcement learning with strategy based goals"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent deep reinforcement learning with human strategies"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "a human mixed strategy approach to deep reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "actor critic policy optimization in partially observable multiagent environments"):
  with members:
    - "combining no regret and q learning"
    - "introduction to online convex optimization"
    - "policy gradient with value function approximation for collective multiagent planning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "a generalised method for empirical game theoretic analysis"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "maintaining cooperation in complex social dilemmas using 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)