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: "lower bound on the computational complexity of discounted markov decision problems"
  Abstract: We study the computational complexity of the infinite-horizon discounted-reward Markov Decision Problem (MDP) with a finite state space $|\mathcal{S}|$ and a finite action space $|\mathcal{A}|$. We show that any randomized algorithm needs a running time at least $\Omega(|\mathcal{S}|^2|\mathcal{A}|)$ to compute an $\epsilon$-optimal policy with high probability. We consider two variants of the MDP where the input is given in specific data structures, including arrays of cumulative probabilities and binary trees of transition probabilities. For these cases, we show that the complexity lower bound reduces to $\Omega\left( \frac{|\mathcal{S}| |\mathcal{A}|}{\epsilon} \right)$. These results reveal a surprising observation that the computational complexity of the MDP depends on the data structure of input.

Connected through hyperedge HE-1 (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"
    - "on the complexity of policy iteration"
    - "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-2 (induced by source paper "variance reduced value iteration and faster algorithms for solving markov decision processes"):
  with members:
    - "on the complexity of policy iteration"
    - "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"

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.CC(Computational Complexity)