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 "v fuzz vulnerability oriented evolutionary fuzzing"):
  Abstract: Fuzzing is a technique of finding bugs by executing a software recurrently with a large number of abnormal inputs. Most of the existing fuzzers consider all parts of a software equally, and pay too much attention on how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this paper, we design and implement a vulnerability-oriented evolutionary fuzzing prototype named V-Fuzz, which aims to find bugs efficiently and quickly in a limited time. V-Fuzz consists of two main components: a neural network-based vulnerability prediction model and a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of the software are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which tend to arrive at the vulnerable locations, guided by the vulnerability prediction result. Experimental results demonstrate that V-Fuzz can find bugs more efficiently than state-of-the-art fuzzers. Moreover, V-Fuzz has discovered 10 CVEs, and 3 of them are newly discovered. We reported the new CVEs, and they have been confirmed and fixed.

Includes members:
  Member "deep reinforcement fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review of machine learning applications in fuzzing")
  Member "vuldeepecker a deep learning based system for vulnerability detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meuzz smart seed scheduling for hybrid 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 "automatic feature learning for vulnerability prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "devign effective vulnerability identification by learning comprehensive program semantics via graph neural networks")
  Member "faster fuzzing reinitialization with deep neural models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neutaint efficient dynamic taint analysis with neural networks")
  Member "neural network based graph embedding for cross platform binary code similarity detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")
  Member "not all bytes are equal neural byte sieve for fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")

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)