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: "superion grammar aware greybox fuzzing"
  Abstract: In recent years, coverage-based greybox fuzzing has proven itself to be one of the most effective techniques for finding security bugs in practice. Particularly, American Fuzzy Lop (AFL for short) is deemed to be a great success in fuzzing relatively simple test inputs. Unfortunately, when it meets structured test inputs such as XML and JavaScript, those grammar-blind trimming and mutation strategies in AFL hinder the effectiveness and efficiency. #R##N#To this end, we propose a grammar-aware coverage-based greybox fuzzing approach to fuzz programs that process structured inputs. Given the grammar (which is often publicly available) of test inputs, we introduce a grammar-aware trimming strategy to trim test inputs at the tree level using the abstract syntax trees (ASTs) of parsed test inputs. Further, we introduce two grammar-aware mutation strategies (i.e., enhanced dictionary-based mutation and tree-based mutation). Specifically, tree-based mutation works via replacing subtrees using the ASTs of parsed test inputs. Equipped with grammar-awareness, our approach can carry the fuzzing exploration into width and depth. #R##N#We implemented our approach as an extension to AFL, named Superion; and evaluated the effectiveness of Superion on real-life large-scale programs (a XML engine libplist and three JavaScript engines WebKit, Jerryscript and ChakraCore). Our results have demonstrated that Superion can improve the code coverage (i.e., 16.7% and 8.8% in line and function coverage) and bug-finding capability (i.e., 31 new bugs, among which we discovered 21 new vulnerabilities with 16 CVEs assigned and 3.2K USD bug bounty rewards received) over AFL and jsfunfuzz. We also demonstrated the effectiveness of our grammar-aware trimming and mutation.

Connected through hyperedge HE-1 (induced by source paper "cagfuzz coverage guided adversarial generative fuzzing testing of deep learning systems"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "semantic fuzzing with zest"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "software reliability growth models predict autonomous vehicle disengagement events"
    - "deephunter hunting deep neural network defects via coverage guided fuzzing"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "testing deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "semantic fuzzing with zest"):
  with members:
    - "smart greybox fuzzing"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach"
    - "angora efficient fuzzing by principled search"
    - "learn fuzz machine learning for input fuzzing"
    - "synthesizing program input grammars"
    - "automatic wsdl guided test case generation for proper testing of web services"

Connected through hyperedge HE-3 (induced by source paper "building fast fuzzers"):
  with members:
    - "full speed fuzzing reducing fuzzing overhead through coverage guided tracing"
    - "learn fuzz machine learning for input fuzzing"
    - "synthesizing program input grammars"

Connected through hyperedge HE-4 (induced by source paper "weizz automatic grey box fuzzing for structured binary formats"):
  with members:
    - "angora efficient fuzzing by principled search"
    - "a survey of symbolic execution techniques"

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.CR(Cryptography and Security)