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: "horn ice learning for synthesizing invariantsand contracts"
  Abstract: We design learning algorithms for synthesizing invariants using Horn implication counterexamples (Horn-ICE), extending the ICE-learning model. In particular, we describe a decision-tree learning algorithm that learns from nonlinear Horn-ICE samples, works in polynomial time, and uses statistical heuristics to learn small trees that satisfy the samples. Since most verification proofs can be modeled using nonlinear Horn clauses, Horn-ICE learning is a more robust technique to learn inductive annotations that prove programs correct. Our experiments show that an implementation of our algorithm is able to learn adequate inductive invariants and contracts efficiently for a variety of sequential and concurrent programs.

Connected through hyperedge HE-1 (induced by source paper "complexity and information in invariant inference"):
  with members:
    - "learning disjunctions of predicates"
    - "interactive program synthesis"
    - "abstract learning frameworks for synthesis"
    - "a theory of formal synthesis via inductive learning"
    - "ic3 modulo theories via implicit predicate abstraction"
    - "abstract acceleration of general linear loops"
    - "tight bounds on proper equivalence query learning of dnf"
    - "interpolant based transition relation approximation"

Connected through hyperedge HE-2 (induced by source paper "overfitting in synthesis theory and practice extended version"):
  with members:
    - "sygus comp 2018 results and analysis"
    - "webrelate integrating web data with spreadsheets using examples"
    - "loopinvgen a loop invariant generator based on precondition inference"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"
    - "a theory of formal synthesis via inductive learning"
    - "the octagon abstract domain"

Connected through hyperedge HE-3 (induced by source paper "synthesizing basic semialgebraic invariants by semidefinite programming"):
  with members:
    - "polynomial invariants for affine programs"
    - "abstraction of elementary hybrid systems by variable transformation"
    - "julia a fresh approach to numerical computing"
    - "generating non linear interpolants by semidefinite programming"
    - "predicate generation for learning based quantifier free loop invariant inference"

Connected through hyperedge HE-4 (induced by source paper "learning based synthesis of safety controllers"):
  with members:
    - "strategy representation by decision trees in reactive synthesis"
    - "an automaton learning approach to solving safety games over infinite graphs"
    - "abstract learning frameworks for synthesis"

Connected through hyperedge HE-5 (induced by source paper "sygus comp 2018 results and analysis"):
  with members:
    - "sygus techniques in the core of an smt solver"
    - "loopinvgen a loop invariant generator based on precondition inference"
    - "language to specify syntax guided synthesis problems"

Connected through hyperedge HE-6 (induced by source paper "synthesizing more expressive invariants by semidefinite programming"):
  with members:
    - "julia a fresh approach to numerical computing"

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)