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: "better quality in synthesis through quantitative objectives"
  Abstract: Most specification languages express only qualitative constraints. #R##N#However, among two implementations that satisfy a given specification, one may be preferred to another. For example, if a specification asks that every request is followed by a response, one may prefer an implementation that generates responses quickly but does not generate unnecessary responses. We use quantitative properties to measure the "goodness" of an implementation. Using games with corresponding quantitative objectives, we can synthesize "optimal" implementations, which are preferred among the set of possible implementations that satisfy a given specification. In particular, we show how automata with lexicographic mean-payoff conditions can be used to express many interesting quantitative properties for reactive systems. In this framework, the synthesis of optimal implementations requires the solution of lexicographic mean-payoff games (for safety requirements), and the solution of games with both lexicographic mean-payoff and parity objectives (for liveness requirements). We present algorithms for solving both kinds of novel graph games.

Connected through hyperedge HE-1 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "batch policy learning under constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-2 (induced by source paper "imitation projected policy gradient for programmatic reinforcement learning"):
  with members:
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-3 (induced by source paper "resource guided program synthesis"):
  with members:
    - "verifying and synthesizing constant resource implementations with types"
    - "program synthesis using abstraction refinement"
    - "program synthesis using conflict driven learning"
    - "towards automatic resource bound analysis for ocaml"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "synthesis of recursive adt transformations from reusable templates"
    - "program synthesis from polymorphic refinement types"
    - "denotational cost semantics for functional languages with inductive types"

Connected through hyperedge HE-4 (induced by source paper "optimally resilient strategies in pushdown safety games"):
  with members:
    - "synthesizing optimally resilient controllers"
    - "summaries for context free games"
    - "an automaton learning approach to solving safety games over infinite graphs"
    - "quantitative games under failures"
    - "robust linear temporal logic"
    - "how to handle assumptions in synthesis"
    - "playing pushdown parity games in a hurry"

Connected through hyperedge HE-5 (induced by source paper "dcsynth guided reactive synthesis with soft requirements"):
  with members:
    - "specification and reactive synthesis of robust controllers"
    - "bosy an experimentation framework for bounded synthesis"
    - "dcsynth guided reactive synthesis with soft requirements for robust controller and shield synthesis"
    - "the 4th reactive synthesis competition syntcomp 2017 benchmarks participants results"
    - "formalizing timing diagram requirements in discrete duration calulus"
    - "average energy games"

Connected through hyperedge HE-6 (induced by source paper "reactive synthesis with maximum realizability of linear temporal logic specifications"):
  with members:
    - "encodings of bounded synthesis"
    - "maximum realizability for linear temporal logic specifications"
    - "robust linear temporal logic"
    - "low effort specification debugging and analysis"
    - "on the minimal revision problem of specification automata"

Connected through hyperedge HE-7 (induced by source paper "spectra a specification language for reactive systems"):
  with members:
    - "performance heuristics for gr 1 synthesis and related algorithms"
    - "synthesizing a lego forklift controller in gr 1 a case study"
    - "permissive controller synthesis for probabilistic systems"

Connected through hyperedge HE-8 (induced by source paper "environmentally friendly gr 1 synthesis"):
  with members:
    - "assume admissible synthesis"
    - "how to handle assumptions in synthesis"

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)