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: "abstraction driven concolic testing"
  Abstract: Concolic testing is a promising method for generating test suites for large programs. However, it suffers from the path-explosion problem and often fails to find tests that cover difficult-to-reach parts of programs. In contrast, model checkers based on counterexample-guided abstraction refinement explore programs exhaustively, while failing to scale on large programs with precision. In this paper, we present a novel method that iteratively combines concolic testing and model checking to find a test suite for a given coverage criterion. If concolic testing fails to cover some test goals, then the model checker refines its program abstraction to prove more paths infeasible, which reduces the search space for concolic testing. We have implemented our method on top of the concolic-testing tool CREST and the model checker CpaChecker. We evaluated our tool on a collection of programs and a category of SvComp benchmarks. In our experiments, we observed an improvement in branch coverage compared to CREST from 48% to 63% in the best case, and from 66% to 71% on average.

Connected through hyperedge HE-1 (induced by source paper "enhancing dynamic symbolic execution by automatically learning search heuristics"):
  with members:
    - "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach"
    - "concolic testing for deep neural networks"
    - "reinforcement learning for automatic test case prioritization and selection in continuous integration"
    - "firmusb vetting usb device firmware using domain informed symbolic execution"
    - "learn fuzz machine learning for input fuzzing"
    - "a survey of symbolic execution techniques"

Connected through hyperedge HE-2 (induced by source paper "verification artifacts in cooperative verification survey and unifying component framework"):
  with members:
    - "model checker execution reports"
    - "cpachecker a tool for configurable software verification"

Connected through hyperedge HE-3 (induced by source paper "towards efficient data flow test data generation"):
  with members:
    - "differentially testing soundness and precision of program analyzers"
    - "time to clean your test objectives"
    - "smartunit empirical evaluations for automated unit testing of embedded software in industry"
    - "cpachecker a tool for configurable software verification"

Connected through hyperedge HE-4 (induced by source paper "combining symbolic execution and model checking to verify mpi programs"):
  with members:
    - "adversarial symbolic execution for detecting concurrency related cache timing leaks"
    - "on the completeness of verifying message passing programs under bounded asynchrony"
    - "static analysis of communicating processes using symbolic transducers"
    - "mpise symbolic execution of mpi programs"

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)