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: "are there good mistakes a theoretical analysis of cegis"
  Abstract: Author(s): Jha, Susmit; Seshia, Sanjit A | Editor(s): Chatterjee, Krishnendu; Ehlers, Rudiger; Jha, Susmit | Abstract: © S. Jha a S.A.Seshia. Counterexample-guided inductive synthesis (CEGIS) is used to synthesize programs from a candidate space of programs. The technique is guaranteed to terminate and synthesize the correct program if the space of candidate programs is finite. But the technique may or may not terminate with the correct program if the candidate space of programs is infinite. In this paper, we perform a theoretical analysis of counterexample-guided inductive synthesis technique. We investigate whether the set of candidate spaces for which the correct program can be synthesized using CEGIS depends on the counterexamples used in inductive synthesis, that is, whether there are good mistakes which would increase the synthesis power. We investigate whether the use of minimal counterexamples instead of arbitrary counterexamples expands the set of candidate spaces of programs for which inductive synthesis can successfully synthesize a correct program. We consider two kinds of counterexamples: minimal counterexamples and history bounded counterexamples. The history bounded counterexample used in any iteration of CEGIS is bounded by the examples used in previous iterations of inductive synthesis. We examine the relative change in power of inductive synthesis in both cases. We show that the synthesis technique using minimal counterexamples MinCEGIS has the same synthesis power as CEGIS but the synthesis technique using history bounded counterexamples HCEGIS has different power than that of CEGIS, but none dominates the other.

Connected through hyperedge HE-1 (induced by source paper "counterexample guided neural synthesis"):
  with members:
    - "neural program synthesis with priority queue training"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "programming not only by example"
    - "deepcoder learning to write programs"
    - "automated synthesis of semantic malware signatures using maximum satisfiability"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "neuro symbolic program synthesis"

Connected through hyperedge HE-2 (induced by source paper "statically verifying continuous integration configurations"):
  with members:
    - "built to last or built too fast evaluating prediction models for build times"
    - "on the interplay between non functional requirements and builds on continuous integration"
    - "learning invariants using decision trees"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-3 (induced by source paper "interpolation based gr 1 assumptions refinement"):
  with members:
    - "a theory of formal synthesis via inductive learning"
    - "counter strategy guided refinement of gr 1 temporal logic specifications"
    - "environment assumptions for synthesis"

Connected through hyperedge HE-4 (induced by source paper "a theory of formal synthesis via inductive learning"):
  with members:
    - "sciduction combining induction deduction and structure for verification and synthesis"
    - "context bounded analysis for concurrent programs with dynamic creation of threads"

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)