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: "sound value iteration"
  Abstract: Computing reachability probabilities is at the heart of probabilistic model checking. All model checkers compute these probabilities in an iterative fashion using value iteration. This technique approximates a fixed point from below by determining reachability probabilities for an increasing number of steps. To avoid results that are significantly off, variants have recently been proposed that converge from both below and above. These procedures require starting values for both sides. We present an alternative that does not require the a priori computation of starting vectors and that converges faster on many benchmarks. The crux of our technique is to give tight and safe bounds - whose computation is cheap - on the reachability probabilities. Lifting this technique to expected rewards is trivial for both Markov chains and MDPs. Experimental results on a large set of benchmarks show its scalability and efficiency.

Connected through hyperedge HE-1 (induced by source paper "symblicit exploration and elimination for probabilistic model checking"):
  with members:
    - "optimistic value iteration"
    - "incremental verification of parametric and reconfigurable markov chains"
    - "accelerated model checking of parametric markov chains"
    - "continuous time markov decisions based on partial exploration"
    - "a storm is coming a modern probabilistic model checker"
    - "explicit model checking of very large mdp using partitioning and secondary storage"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "stopping criteria for value and strategy iteration on concurrent stochastic reachability games"):
  with members:
    - "a storm is coming a modern probabilistic model checker"
    - "verification of markov decision processes using learning algorithms"
    - "strategy improvement for concurrent reachability and safety games"
    - "recursive concurrent stochastic games"
    - "termination criteria for solving concurrent safety and reachability games"

Connected through hyperedge HE-3 (induced by source paper "optimistic value iteration"):
  with members:
    - "a storm is coming a modern probabilistic model checker"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "on the complexity of value iteration"):
  with members:
    - "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"
    - "on the complexity of policy iteration"
    - "the complexity of plan existence and evaluation in probabilistic domains"

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.LO(Logic in Computer Science)