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: "deep reinforcement fuzzing"
  Abstract: Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. We have implemented this new approach, and preliminary empirical evidence shows that reinforcement fuzzing can outperform baseline random fuzzing.

Connected through hyperedge HE-1 (induced by source paper "a review of machine learning applications in fuzzing"):
  with members:
    - "optimizing seed inputs in fuzzing with machine learning"
    - "learning a sat solver from single bit supervision"
    - "evaluating fuzz testing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"
    - "automatic heap layout manipulation for exploitation"
    - "graph neural networks and boolean satisfiability"
    - "deep reinforcement learning a brief survey"

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"
    - "adaptive grey box fuzz testing with thompson sampling"
    - "fuzzergym a competitive framework for fuzzing and learning"
    - "smartseed smart seed generation for efficient fuzzing"
    - "recurrent neural networks for fuzz testing web browsers"
    - "improving grey box fuzzing by modeling program behavior"

Connected through hyperedge HE-3 (induced by source paper "using deep learning to solve computer security challenges a survey"):
  with members:
    - "deepcheck a non intrusive control flow integrity checking based on deep learning"
    - "convolutional neural networks over control flow graphs for software defect prediction"
    - "recurrent neural network attention mechanisms for interpretable system log anomaly detection"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "ropnn detection of rop payloads using deep neural networks"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-4 (induced by source paper "v fuzz vulnerability oriented evolutionary fuzzing"):
  with members:
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "automatic feature learning for vulnerability prediction"
    - "faster fuzzing reinitialization with deep neural models"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-5 (induced by source paper "matryoshka fuzzing deeply nested branches"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "angora efficient fuzzing by principled search"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "learn fuzz machine learning for input fuzzing"

Connected through hyperedge HE-6 (induced by source paper "neuzz efficient fuzzing with neural program smoothing"):
  with members:
    - "neuro symbolic execution the feasibility of an inductive approach to symbolic execution"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "faster fuzzing reinitialization with deep neural models"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "measuring catastrophic forgetting in neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-7 (induced by source paper "greybox fuzzing as a contextual bandits problem"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "an exploratory survey of hybrid testing techniques involving symbolic execution and fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "ropnn detection of rop payloads using deep neural networks"):
  with members:
    - "control flow integrity precision security and performance"
    - "deep learning in neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional neural networks for sentence classification"
    - "cryptographically enforced control flow integrity"

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.AI(Artificial Intelligence)