human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neuzz efficient fuzzing with neural program smoothing"):
  Abstract: Fuzzing has become the de facto standard technique for finding software vulnerabilities. However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs. Most popular fuzzers use evolutionary guidance to generate inputs that can trigger different bugs. Such evolutionary algorithms, while fast and simple to implement, often get stuck in fruitless sequences of random mutations. Gradient-guided optimization presents a promising alternative to evolutionary guidance. Gradient-guided techniques have been shown to significantly outperform evolutionary algorithms at solving high-dimensional structured optimization problems in domains like machine learning by efficiently utilizing gradients or higher-order derivatives of the underlying function. However, gradient-guided approaches are not directly applicable to fuzzing as real-world program behaviors contain many discontinuities, plateaus, and ridges where the gradient-based methods often get stuck. We observe that this problem can be addressed by creating a smooth surrogate function approximating the discrete branching behavior of target program. In this paper, we propose a novel program smoothing technique using surrogate neural network models that can incrementally learn smooth approximations of a complex, real-world program's branching behaviors. We further demonstrate that such neural network models can be used together with gradient-guided input generation schemes to significantly improve the fuzzing efficiency. Our extensive evaluations demonstrate that NEUZZ significantly outperforms 10 state-of-the-art graybox fuzzers on 10 real-world programs both at finding new bugs and achieving higher edge coverage. NEUZZ found 31 unknown bugs that other fuzzers failed to find in 10 real world programs and achieved 3X more edge coverage than all of the tested graybox fuzzers for 24 hours running.

Includes members:
  Member "neuro symbolic execution the feasibility of an inductive approach to symbolic execution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neutaint efficient dynamic taint analysis with neural networks")
  Member "deep reinforcement fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a review of machine learning applications in fuzzing")
  Member "angora efficient fuzzing by principled search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hotfuzz discovering algorithmic denial of service vulnerabilities through guided micro fuzzing")
  Member "faster fuzzing reinitialization with deep neural models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neutaint efficient dynamic taint analysis with neural networks")
  Member "neurogenesis deep learning extending deep networks to accommodate new classes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on catastrophic interference in atari 2600 games")
  Member "not all bytes are equal neural byte sieve for fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "measuring catastrophic forgetting in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")

Question: Please tell me which class the source paper 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.CR(Cryptography and Security)