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: "on the complexity of policy iteration"
  Abstract: Decision-making problems in uncertain or stochastic domains are often formulated as Markov decision processes (MDPs). Policy iteration (PI) is a popular algorithm for searching over policy-space, the size of which is exponential in the number of states. We are interested in bounds on the complexity of PI that do not depend on the value of the discount factor. In this paper we prove the first such non-trivial, worst-case, upper bounds on the number of iterations required by PI to converge to the optimal policy. Our analysis also sheds new light on the manner in which PI progresses through the space of policies.

Connected through hyperedge HE-1 (induced by source paper "solving discounted stochastic two player games with near optimal time and sample complexity"):
  with members:
    - "on the optimality of sparse model based planning for markov decision processes"
    - "online reinforcement learning in stochastic games"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"

Connected through hyperedge HE-2 (induced by source paper "the value function polytope in reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "on the complexity of solving markov decision problems"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of value iteration"):
  with members:
    - "sound value iteration"
    - "the reachability problem for petri nets is not elementary"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "value iteration networks"
    - "robust synchronization in markov decision processes"
    - "the complexity of the simplex method"
    - "on the complexity of solving markov decision problems"
    - "the complexity of plan existence and evaluation in probabilistic domains"

Connected through hyperedge HE-4 (induced by source paper "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"):
  with members:
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "lower bound on the computational complexity of discounted markov decision problems"
    - "on the sample complexity of reinforcement learning with a generative model"
    - "pac bounds for discounted mdps"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-5 (induced by source paper "variance reduced value iteration and faster algorithms for solving markov decision processes"):
  with members:
    - "lower bound on the computational complexity of discounted markov decision problems"
    - "on the sample complexity of reinforcement learning with a generative model"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"

Connected through hyperedge HE-6 (induced by source paper "lower bound on the computational complexity of discounted markov decision problems"):
  with members:
    - "on the sample complexity of reinforcement learning with a generative model"
    - "pac bounds for discounted mdps"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-7 (induced by source paper "improved bound on the worst case complexity of policy iteration"):
  with members:
    - "a complexity analysis of policy iteration through combinatorial matrices arising from unique sink orientations"
    - "pagerank optimization by edge selection"
    - "the value iteration algorithm is not strongly polynomial for discounted dynamic programming"
    - "the simplex method is strongly polynomial for deterministic markov decision processes"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"
    - "exponential lower bounds for policy iteration"

Connected through hyperedge HE-8 (induced by source paper "a learning theoretic approach to energy harvesting communication system optimization"):
  with members:
    - "throughput maximization for the gaussian relay channel with energy harvesting constraints"
    - "throughput maximization for an energy harvesting communication system with processing cost"
    - "sum rate optimal power policies for energy harvesting transmitters in an interference channel"
    - "two hop communication with energy harvesting"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "a general framework for the optimization of energy harvesting communication systems with battery imperfections"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal packet scheduling on an energy harvesting broadcast link"

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)