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: "learning universally quantified invariants of linear data structures"
  Abstract: We propose a new automaton model, called quantified data automata over words, that can model quantified invariants over linear data structures, and build poly-time active learning algorithms for them, where the learner is allowed to query the teacher with membership and equivalence queries. In order to express invariants in decidable logics, we invent a decidable subclass of QDAs, called elastic QDAs, and prove that every QDA has a unique minimally-over-approximating elastic QDA. We then give an application of these theoretically sound and efficient active learning algorithms in a passive learning framework and show that we can efficiently learn quantified linear data structure invariants from samples obtained from dynamic runs for a large class of programs.

Connected through hyperedge HE-1 (induced by source paper "compositional verification of heap manipulating programs through property guided learning"):
  with members:
    - "sling using dynamic analysis to infer program invariants in separation logic"
    - "concolic testing heap manipulating programs"
    - "enhancing symbolic execution of heap based programs with separation logic for test input generation"
    - "gated graph sequence neural networks"
    - "fully automated shape analysis based on forest automata"
    - "cpachecker a tool for configurable software verification"
    - "the octagon abstract domain"

Connected through hyperedge HE-2 (induced by source paper "horn ice learning for synthesizing invariantsand contracts"):
  with members:
    - "invariant synthesis for incomplete verification engines"

Connected through hyperedge HE-3 (induced by source paper "invariant synthesis for incomplete verification engines"):
  with members:
    - "learning invariants using decision trees"

Connected through hyperedge HE-4 (induced by source paper "abstract learning frameworks for synthesis"):
  with members:
    - "from non preemptive to preemptive scheduling using synchronization 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.PL(Programming Languages)