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: "synthesizing program input grammars"
  Abstract: We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program. Our algorithm addresses shortcomings of existing grammar inference algorithms, which both severely overgeneralize and are prohibitively slow. Our implementation, GLADE, leverages the grammar synthesized by our algorithm to fuzz test programs with structured inputs. We show that GLADE substantially increases the incremental coverage on valid inputs compared to two baseline fuzzers.

Connected through hyperedge HE-1 (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-2 (induced by source paper "synthesizing queries via interactive sketching"):
  with members:
    - "selecting representative examples for program synthesis"
    - "active learning of points to specifications"
    - "program synthesis using conflict driven learning"
    - "interactive program synthesis"
    - "interactive bayesian hierarchical clustering"
    - "deepcoder learning to write programs"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"

Connected through hyperedge HE-3 (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"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "deepcoder learning to write programs"

Connected through hyperedge HE-4 (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"
    - "superion grammar aware greybox fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "automatic wsdl guided test case generation for proper testing of web services"

Connected through hyperedge HE-5 (induced by source paper "inferring input grammars from dynamic control flow"):
  with members:
    - "building fast fuzzers"
    - "sample free learning of input grammars for comprehensive software fuzzing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "active learning of input grammars"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "trx a formally verified parser interpreter"

Connected through hyperedge HE-6 (induced by source paper "efficient synthesis with probabilistic constraints"):
  with members:
    - "bayesian synthesis of probabilistic programs for automatic data modeling"
    - "network wide configuration synthesis"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-7 (induced by source paper "building fast fuzzers"):
  with members:
    - "full speed fuzzing reducing fuzzing overhead through coverage guided tracing"
    - "superion grammar aware greybox fuzzing"
    - "learn fuzz machine learning for input fuzzing"

Connected through hyperedge HE-8 (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"
    - "faster fuzzing reinitialization with deep neural models"
    - "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"

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.PL(Programming Languages)