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: "learning inputs in greybox fuzzing"
  Abstract: Greybox fuzzing is a lightweight testing approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by complex checks. #R##N#In this paper, we present a technique that extends greybox fuzzing with a method for learning new inputs based on already explored program executions. These inputs can be learned such that they guide exploration toward specific executions, for instance, ones that increase path coverage or reveal vulnerabilities. We have evaluated our technique and compared it to traditional greybox fuzzing on 26 real-world benchmarks. In comparison, our technique significantly increases path coverage (by up to 3X) and detects more bugs (up to 38% more), often orders-of-magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "targeted greybox fuzzing with static lookahead analysis"):
  with members:
    - "v fuzz vulnerability oriented evolutionary fuzzing"
    - "harvey a greybox fuzzer for smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "online detection of effectively callback free objects with applications to smart contracts"
    - "quantitative analysis of smart contracts"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"

Connected through hyperedge HE-2 (induced by source paper "oracle supported dynamic exploit generation for smart contracts"):
  with members:
    - "precise attack synthesis for smart contracts"
    - "exploiting the laws of order in smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "sereum protecting existing smart contracts against re entrancy attacks"
    - "quantitative analysis of smart contracts"
    - "evaluating fuzz testing"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "towards safer smart contracts a survey of languages and verification methods"

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)