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: "fuzzergym a competitive framework for fuzzing and learning"
  Abstract: Fuzzing is a commonly used technique designed to test software by automatically crafting program inputs. Currently, the most successful fuzzing algorithms emphasize simple, low-overhead strategies with the ability to efficiently monitor program state during execution. Through compile-time instrumentation, these approaches have access to numerous aspects of program state including coverage, data flow, and heterogeneous fault detection and classification. However, existing approaches utilize blind random mutation strategies when generating test inputs. We present a different approach that uses this state information to optimize mutation operators using reinforcement learning (RL). By integrating OpenAI Gym with libFuzzer we are able to simultaneously leverage advancements in reinforcement learning as well as fuzzing to achieve deeper coverage across several varied benchmarks. Our technique connects the rich, efficient program monitors provided by LLVM Santizers with a deep neural net to learn mutation selection strategies directly from the input data. The cross-language, asynchronous architecture we developed enables us to apply any OpenAI Gym compatible deep reinforcement learning algorithm to any fuzzing problem with minimal slowdown.

Connected through hyperedge HE-1 (induced by source paper "efficient black box assessment of autonomous vehicle safety"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adaptive stress testing for autonomous vehicles"
    - "verifai a toolkit for the design and analysis of artificial intelligence based systems"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "deep imitative models for flexible inference planning and control"
    - "ai benchmark running deep neural networks on android smartphones"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "adaptive stress testing finding failure events with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a systematic review of fuzzing based on machine learning techniques"):
  with members:
    - "capturing the symptoms of malicious code in electronic documents by file s entropy signal combined with machine learning"
    - "optimizing seed inputs in fuzzing with machine learning"
    - "v fuzz vulnerability oriented evolutionary fuzzing"
    - "deep reinforcement fuzzing"
    - "adaptive grey box fuzz testing with thompson sampling"
    - "smartseed smart seed generation for efficient fuzzing"
    - "recurrent neural networks for fuzz testing web browsers"
    - "improving grey box fuzzing by modeling program behavior"

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)