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: "not all bytes are equal neural byte sieve for fuzzing"
  Abstract: Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input designed to cause crashes, buffer overflows, memory errors, and exceptions. Crafting malicious inputs in an efficient manner is a difficult open problem and often the best approach to generating such inputs is through applying uniform random mutations to pre-existing valid inputs (seed files). We present a learning technique that uses neural networks to learn patterns in the input files from past fuzzing explorations to guide future fuzzing explorations. In particular, the neural models learn a function to predict good (and bad) locations in input files to perform fuzzing mutations based on the past mutations and corresponding code coverage information. We implement several neural models including LSTMs and sequence-to-sequence models that can encode variable length input files. We incorporate our models in the state-of-the-art AFL (American Fuzzy Lop) fuzzer and show significant improvements in terms of code coverage, unique code paths, and crashes for various input formats including ELF, PNG, PDF, and XML.

Connected through hyperedge HE-1 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing"):
  with members:
    - "a review of machine learning applications in fuzzing"
    - "ptrix efficient hardware assisted fuzzing for cots binary"
    - "savior towards bug driven hybrid testing"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "neuzz efficient fuzzing with neural program smoothing"

Connected through hyperedge HE-2 (induced by source paper "learning transferable graph exploration"):
  with members:
    - "synthetic datasets for neural program synthesis"
    - "dom q net grounded rl on structured language"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "learning to search with mctsnets"
    - "meta reinforcement learning of structured exploration strategies"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a review of machine learning applications in fuzzing"):
  with members:
    - "optimizing seed inputs in fuzzing with machine learning"
    - "deep reinforcement fuzzing"
    - "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"

Connected through hyperedge HE-5 (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-6 (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"
    - "deep reinforcement fuzzing"
    - "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"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-7 (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"
    - "faster fuzzing reinitialization with deep neural models"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-8 (induced by source paper "synfuzz efficient concolic execution via branch condition synthesis"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "program synthesis using conflict driven learning"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "learn fuzz machine learning for input fuzzing"
    - "deepcoder learning to write programs"
    - "synthesizing program input grammars"

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.SE(Software Engineering)