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: "knowledge spaces and the completeness of learning strategies"
  Abstract: We propose a theory of learning aimed to formalize some ideas underlying Co- quand's game semantics and Krivine's realizability of classical logic. We introduce a notion of knowledge state together with a new topology, capturing finite positive and negative information that guides a learning strategy. We use a leading example to illustrate how non-constructive proofs lead to continuous and effective learning strategies over knowledge spaces, and prove that our learning semantics is sound and complete w.r.t. classical truth, as it is the case for Coquand's and Krivine's approaches.

Connected through hyperedge HE-1 (induced by source paper "multi weighted markov decision processes with reachability objectives"):
  with members:
    - "stochastic shortest paths and weight bounded properties in markov decision processes"
    - "stream differential equations specification formats and solution methods"
    - "threshold constraints with guarantees for parity objectives in markov decision processes"
    - "maximizing the conditional expected reward for reaching the goal"
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "variations on the stochastic shortest path problem"
    - "the odds of staying on budget"

Connected through hyperedge HE-2 (induced by source paper "no value restriction is needed for algebraic effects and handlers"):
  with members:
    - "on the expressive power of user defined effects effect handlers monadic reflection delimited control"
    - "efficient algebraic effect handlers for prolog"
    - "do be do be do"
    - "trace semantics for polymorphic references"
    - "programming with algebraic effects and handlers"
    - "inferring algebraic effects"
    - "koka programming with row polymorphic effect types"
    - "an effect system for algebraic effects and handlers"

Connected through hyperedge HE-3 (induced by source paper "value iteration for long run average reward in markov decision processes"):
  with members:
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "the complexity of ergodic mean payoff games"
    - "verification of markov decision processes using learning algorithms"
    - "markov decision processes with multiple long run averageobjectives"
    - "faster algorithms for markov decision processes with low treewidth"
    - "trading performance for stability in markov decision processes"
    - "assume guarantee abstraction refinement for probabilistic systems"

Connected through hyperedge HE-4 (induced by source paper "finite model reasoning over existential rules"):
  with members:
    - "query rewriting and optimization for ontological databases"
    - "querying the guarded fragment"
    - "ontology based data access a study through disjunctive datalog csp and mmsnp"
    - "converging to the chase a tool for finite controllability"

Connected through hyperedge HE-5 (induced by source paper "from propositional logic to plausible reasoning a uniqueness theorem"):
  with members:
    - "representations of measurable sets in computable measure theory"
    - "cox s theorem revisited"
    - "a counterexample to theorems of cox and fine"

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)