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: "interactive small step algorithms ii abstract state machines and the characterization theorem"
  Abstract: In earlier work, the Abstract State Machine Thesis -- that arbitrary#N#algorithms are behaviorally equivalent to abstract state machines -- was#N#established for several classes of algorithms, including ordinary, interactive,#N#small-step algorithms. This was accomplished on the basis of axiomatizations of#N#these classes of algorithms. In Part I (Interactive Small-Step Algorithms I:#N#Axiomatization), the axiomatization was extended to cover interactive#N#small-step algorithms that are not necessarily ordinary. This means that the#N#algorithms (1) can complete a step without necessarily waiting for replies to#N#all queries from that step and (2) can use not only the environment's replies#N#but also the order in which the replies were received. In order to prove the#N#thesis for algorithms of this generality, we extend here the definition of#N#abstract state machines to incorporate explicit attention to the relative#N#timing of replies and to the possible absence of replies. We prove the#N#characterization theorem for extended abstract state machines with respect to#N#general algorithms as axiomatized in Part I.

Connected through hyperedge HE-1 (induced by source paper "qualitative analysis of concurrent mean payoff games"):
  with members:
    - "the complexity of partial observation stochastic parity games with finite memory strategies"
    - "the value 1 problem under finite memory strategies for concurrent mean payoff games"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "synthesis from ltl specifications with mean payoff objectives"
    - "decidable problems for probabilistic automata on infinite words"
    - "partial observation stochastic games how to win when belief fails"
    - "qualitative reachability in stochastic bpa games"
    - "measuring and synthesizing systems in probabilistic environments"

Connected through hyperedge HE-2 (induced by source paper "pomdps under probabilistic semantics"):
  with members:
    - "my brain is full when more memory helps"
    - "learning finite state controllers for partially observable environments"
    - "solving pomdps by searching the space of finite policies"
    - "heuristic search value iteration for pomdps"
    - "randomness for free"
    - "qualitative analysis of partially observable markov decision processes"
    - "algorithms for omega regular games with imperfect information"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-3 (induced by source paper "the generic model of computation"):
  with members:
    - "evolving algebras 1993 lipari guide"
    - "when are two algorithms the same"
    - "interactive small step algorithms i axiomatization"

Connected through hyperedge HE-4 (induced by source paper "low level dichotomy for quantified constraint satisfaction problems"):
  with members:
    - "a formal foundation for xrml"
    - "a characterisation of first order constraint satisfaction problems"
    - "the complexity of quantified constraint satisfaction collapsibility sink algebras and the three element case"

Connected through hyperedge HE-5 (induced by source paper "randomness for free"):
  with members:
    - "pomdps under probabilistic semantics"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "partial observation stochastic games how to win when belief fails"
    - "qualitative concurrent stochastic games with imperfect information"
    - "algorithms for omega regular games with imperfect information"

Connected through hyperedge HE-6 (induced by source paper "actorscript tm extension of c sharp tm java tm and objective c tm iadaptive tm concurrency for anticloud tm privacy and security"):
  with members:
    - "actor model of computation scalable robust information systems"
    - "inconsistency robustness in foundations mathematics self proves its own consistency and other matters"

Connected through hyperedge HE-7 (induced by source paper "actor model of computation scalable robust information systems"):
  with members:
    - "formalizing common sense for scalable inconsistency robust information integration using direct logic tm reasoning and the actor model"

Connected through hyperedge HE-8 (induced by source paper "antichains for the automata based approach to model checking"):
  with members:
    - "algorithms for omega regular games with imperfect information"

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)