human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "complexity and information in invariant inference"):
  Abstract: This paper addresses the complexity of SAT-based invariant inference, a prominent approach to safety verification. We consider the problem of inferring an inductive invariant of polynomial length given a transition system and a safety property. We analyze the complexity of this problem in a black-box model, called the Hoare-query model, which is general enough to capture algorithms such as IC3/PDR and its variants. An algorithm in this model learns about the system's reachable states by querying the validity of Hoare triples. #R##N#We show that in general an algorithm in the Hoare-query model requires an exponential number of queries. Our lower bound is information-theoretic and applies even to computationally unrestricted algorithms, showing that no choice of generalization from the partial information obtained in a polynomial number of Hoare queries can lead to an efficient invariant inference procedure in this class. #R##N#We then show, for the first time, that by utilizing rich Hoare queries, as done in PDR, inference can be exponentially more efficient than approaches such as ICE learning, which only utilize inductiveness checks of candidates. We do so by constructing a class of transition systems for which a simple version of PDR with a single frame infers invariants in a polynomial number of queries, whereas every algorithm using only inductiveness checks and counterexamples requires an exponential number of queries. #R##N#Our results also shed light on connections and differences with the classical theory of exact concept learning with queries, and imply that learning from counterexamples to induction is harder than classical exact learning from labeled examples. This demonstrates that the convergence rate of Counterexample-Guided Inductive Synthesis depends on the form of counterexamples.

Includes members:
  Member "horn ice learning for synthesizing invariantsand contracts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in synthesis theory and practice extended version")
  Member "learning disjunctions of predicates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive exact learning of decision trees from membership queries")
  Member "interactive program synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "synthesizing queries via interactive sketching")
  Member "abstract learning frameworks for synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decidable synthesis of programs with uninterpreted functions")
  Member "a theory of formal synthesis via inductive learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analyzing cyber physical systems from the perspective of artificial intelligence")
  Member "ic3 modulo theories via implicit predicate abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pridemm a solver for relaxed memory models")
  Member "abstract acceleration of general linear loops":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a calculus for modular loop acceleration")
  Member "tight bounds on proper equivalence query learning of dnf":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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.PL(Programming Languages)