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 computational complexity of stochastic controller optimization in pomdps"
  Abstract: We show that the problem of finding an optimal stochastic 'blind' controller in a Markov decision process is an NP-hard problem. The corresponding decision problem is NP-hard, in PSPACE, and SQRT-SUM-hard, hence placing it in NP would imply breakthroughs in long-standing open problems in computer science. Our result establishes that the more general problem of stochastic controller optimization in POMDPs is also NP-hard. Nonetheless, we outline a special case that is convex and admits efficient global solutions.

Connected through hyperedge HE-1 (induced by source paper "counterexample driven synthesis for probabilistic program sketches"):
  with members:
    - "shepherding hordes of markov chains"
    - "structured synthesis for probabilistic systems"
    - "safety aware apprenticeship learning"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "high level counterexamples for probabilistic automata"
    - "safety constrained reinforcement learning for mdps"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of reachability in parametric markov decision processes"):
  with members:
    - "parameter synthesis for markov models"
    - "qualitative reachability for open interval markov chains"
    - "accelerated model checking of parametric markov chains"
    - "sea param exploring schedulers in parametric mdps"
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"

Connected through hyperedge HE-3 (induced by source paper "counterexample guided strategy improvement for pomdps using recurrent neural networks"):
  with members:
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "a storm is coming a modern probabilistic model checker"
    - "deep recurrent q learning for partially observable mdps"
    - "learning finite state controllers for partially observable environments"

Connected through hyperedge HE-4 (induced by source paper "secure control under partial observability with temporal logic constraints"):
  with members:
    - "the complexity of partial observation stochastic parity games with finite memory strategies"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"

Connected through hyperedge HE-5 (induced by source paper "human in the loop synthesis for partially observable markov decision processes"):
  with members:
    - "permissive finite state controllers of pomdps using parameter synthesis"
    - "a storm is coming a modern probabilistic model checker"
    - "motion planning under partial observability using game based abstraction"
    - "synthesis of shared control protocols with provable safety and performance guarantees"
    - "high level counterexamples for probabilistic automata"
    - "optimal cost almost sure reachability in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "solving pomdps by searching the space of finite policies"

Connected through hyperedge HE-6 (induced by source paper "permissive finite state controllers of pomdps using parameter synthesis"):
  with members:
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "safety constrained reinforcement learning for mdps"
    - "permissive controller synthesis for probabilistic systems"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "optimal cost almost sure reachability in pomdps"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning in rich observation mdps using spectral methods"):
  with members:
    - "experimental results reinforcement learning of pomdps using spectral methods"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "a pac rl algorithm for episodic pomdps"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-8 (induced by source paper "stochastic control via entropy compression"):
  with members:
    - "commutativity in the random walk formulation of the lovasz local lemma"
    - "an algorithmic proof of the lovasz local lemma via resampling oracles"
    - "the computational complexity of probabilistic planning"
    - "reinforcement learning a survey"

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)