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: "faster fuzzing reinitialization with deep neural models"
  Abstract: We improve the performance of the American Fuzzy Lop (AFL) fuzz testing framework by using Generative Adversarial Network (GAN) models to reinitialize the system with novel seed files. We assess performance based on the temporal rate at which we produce novel and unseen code paths. We compare this approach to seed file generation from a random draw of bytes observed in the training seed files. The code path lengths and variations were not sufficiently diverse to fully replace AFL input generation. However, augmenting native AFL with these additional code paths demonstrated improvements over AFL alone. Specifically, experiments showed the GAN was faster and more effective than the LSTM and out-performed a random augmentation strategy, as measured by the number of unique code paths discovered. GAN helps AFL discover 14.23% more code paths than the random strategy in the same amount of CPU time, finds 6.16% more unique code paths, and finds paths that are on average 13.84% longer. Using GAN shows promise as a reinitialization strategy for AFL to help the fuzzer exercise deep paths in software.

Connected through hyperedge HE-1 (induced by source paper "neutaint efficient dynamic taint analysis with neural networks"):
  with members:
    - "neuro symbolic execution the feasibility of an inductive approach to symbolic execution"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "smartseed smart seed generation for efficient fuzzing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"
    - "robustfill neural program learning under noisy i o"
    - "end to end prediction of buffer overruns from raw source code via neural memory networks"
    - "axiomatic attribution for deep networks"

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"
    - "fuzzergym a competitive framework for fuzzing and learning"
    - "smartseed smart seed generation for efficient fuzzing"
    - "recurrent neural networks for fuzz testing web browsers"

Connected through hyperedge HE-3 (induced by source paper "v fuzz vulnerability oriented evolutionary fuzzing"):
  with members:
    - "deep reinforcement fuzzing"
    - "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"
    - "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-4 (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"
    - "deep reinforcement fuzzing"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "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-5 (induced by source paper "smartseed smart seed generation for efficient fuzzing"):
  with members:
    - "angora efficient fuzzing by principled search"
    - "unsupervised image to image translation networks"
    - "generative adversarial networks as variational training of energy based models"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "toward multimodal image to image translation"
    - "learn fuzz machine learning for input fuzzing"
    - "plug play generative networks conditional iterative generation of images in latent space"

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)