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 to cooperate via policy search"
  Abstract: Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to value-based methods for partially observable environments. In this paper, we provide a gradient-based distributed policy-search method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain.

Connected through hyperedge HE-1 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "revealing much while saying less predictive wireless for status update"):
  with members:
    - "age of information vs value of information scheduling for cellular networked control systems"
    - "sampling for remote estimation through queues age of information and beyond"
    - "ai assisted low information latency wireless networking"
    - "status from a random field how densely should one update"
    - "closed form whittle s index enabled random access for timely status update"
    - "minimizing the age of information in a csma environment"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"

Connected through hyperedge HE-3 (induced by source paper "optimization for reinforcement learning from single agent to cooperative agents"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "addressing function approximation error in actor critic methods"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "multi agent reinforcement learning via double averaging primal dual optimization"

Connected through hyperedge HE-4 (induced by source paper "option critic in cooperative multi agent systems"):
  with members:
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "the option critic architecture"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-5 (induced by source paper "rethinking formal models of partially observable multiagent decision making"):
  with members:
    - "problems with the efg formalism a solution attempt using observations"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "avoiding wireheading with value reinforcement learning"
    - "timeability of extensive form games"
    - "solving imperfect information games using decomposition"
    - "decentralized stochastic control with partial history sharing a common information approach"

Connected through hyperedge HE-6 (induced by source paper "distributed policy learning based random access for diversified qos requirements"):
  with members:
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "proximal policy optimization algorithms"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in wireless networks"

Connected through hyperedge HE-7 (induced by source paper "multiagent soft q learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "taming the noise in reinforcement learning via soft updates"
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "policy gradient with value function approximation for collective multiagent planning"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "learning functions across many orders of magnitudes"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "individual planning in agent populations exploiting anonymity and frame action hypergraphs"
    - "planning for decentralized control of multiple robots under uncertainty"

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)