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: "reinforcement learning for automatic test case prioritization and selection in continuous integration"
  Abstract: Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.

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"
    - "firmusb vetting usb device firmware using domain informed symbolic execution"
    - "learn fuzz machine learning for input fuzzing"
    - "a survey of symbolic execution techniques"
    - "abstraction driven concolic testing"

Connected through hyperedge HE-2 (induced by source paper "an approach and benchmark to detect behavioral changes of commits in continuous integration"):
  with members:
    - "automatic test improvement with dspot a study with ten mature open source projects"
    - "bears an extensible java bug benchmark for automatic program repair studies"
    - "a comprehensive study of pseudo tested methods"
    - "shadow symbolic execution with java pathfinder"
    - "how to design a program repair bot insights from the repairnator project"

Connected through hyperedge HE-3 (induced by source paper "multi cycle assignment problems with rotational diversity"):
  with members:
    - "rotational diversity in multi cycle assignment problems"
    - "time aware test case execution scheduling for cyber physical systems"
    - "scheduling a dynamic aircraft repair shop with limited repair resources"
    - "robust local search for solving rcpsp max with durational uncertainty"
    - "bin completion algorithms for multicontainer packing knapsack and covering problems"
    - "negotiating socially optimal allocations of resources"

Connected through hyperedge HE-4 (induced by source paper "test prioritization in continuous integration environments"):
  with members:
    - "normalized compression distance of multisets with applications"
    - "test set diameter quantifying the diversity of sets of test cases"
    - "the similarity metric"

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.SE(Software Engineering)