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: "abstract learning frameworks for synthesis"
  Abstract: We develop abstract learning frameworks (ALFs) for synthesis that embody the principles of CEGIS (counter-example based inductive synthesis) strategies that have become widely applicable in recent years. Our framework defines a general abstract framework of iterative learning, based on a hypothesis space that captures the synthesized objects, a sample space that forms the space on which induction is performed, and a concept space that abstractly defines the semantics of the learning process. We show that a variety of synthesis algorithms in current literature can be embedded in this general framework. While studying these embeddings, we also generalize some of the synthesis problems these instances are of, resulting in new ways of looking at synthesis problems using learning. We also investigate convergence issues for the general framework, and exhibit three recipes for convergence in finite time. The first two recipes generalize current techniques for convergence used by existing synthesis engines. The third technique is a more involved technique of which we know of no existing instantiation, and we instantiate it to concrete synthesis problems.

Connected through hyperedge HE-1 (induced by source paper "decidable synthesis of programs with uninterpreted functions"):
  with members:
    - "proving unrealizability for syntax guided synthesis"
    - "a decidable fragment of second order logic with applications to synthesis"
    - "bounded synthesis of register transducers"
    - "decidable verification of uninterpreted programs"
    - "program synthesis using abstraction refinement"
    - "a theory of formal synthesis via inductive learning"
    - "what s decidable about syntax guided synthesis"
    - "distributed synthesis for acyclic architectures"

Connected through hyperedge HE-2 (induced by source paper "complexity and information in invariant inference"):
  with members:
    - "horn ice learning for synthesizing invariantsand contracts"
    - "learning disjunctions of predicates"
    - "interactive program 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-3 (induced by source paper "joint inference of reward machines and policies for reinforcement learning"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "modular multitask reinforcement learning with policy sketches"
    - "reinforcement learning with temporal logic rewards"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-4 (induced by source paper "learning definable hypotheses on trees"):
  with members:
    - "learning first order definable concepts over structures of small degree"
    - "machine learning with guarantees using descriptive complexity and smt solvers"
    - "learning and verifying quantified boolean queries by example"

Connected through hyperedge HE-5 (induced by source paper "learning based synthesis of safety controllers"):
  with members:
    - "horn ice learning for synthesizing invariantsand contracts"
    - "strategy representation by decision trees in reactive synthesis"
    - "an automaton learning approach to solving safety games over infinite graphs"

Connected through hyperedge HE-6 (induced by source paper "learning first order definable concepts over structures of small degree"):
  with members:
    - "machine learning with guarantees using descriptive complexity and smt solvers"
    - "learning and verifying quantified boolean queries by example"
    - "first order query evaluation on structures of bounded degree"

Connected through hyperedge HE-7 (induced by source paper "learning mso definable hypotheses on string"):
  with members:
    - "machine learning with guarantees using descriptive complexity and smt solvers"

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)