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: "harvey a greybox fuzzer for smart contracts"
  Abstract: We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation 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 narrow checks, which are satisfied by no more than a few input values. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while at the same time keeping the search space and test suite tractable. In this experience paper, we explain how Harvey alleviates both challenges with two key fuzzing techniques and distill the main lessons learned. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that the underlying techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster; they also reveal new insights about contract code.

Connected through hyperedge HE-1 (induced by source paper "ae gis shielding vulnerable smart contracts against attacks"):
  with members:
    - "slither a static analysis framework for smart contracts"
    - "soliditycheck quickly detecting smart contract problems through regular expressions"
    - "exploiting the laws of order in smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "towards safer smart contracts a sequence learning approach to detecting security threats"
    - "towards safer smart contracts a sequence learning approach to detecting vulnerabilities"

Connected through hyperedge HE-2 (induced by source paper "attacking the defi ecosystem with flash loans for fun and profit"):
  with members:
    - "libra fair order matching for electronic financial exchanges"
    - "flash boys 2 0 frontrunning transaction reordering and consensus instability in decentralized exchanges"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "securify practical security analysis of smart contracts"
    - "bitcoin ng a scalable blockchain protocol"

Connected through hyperedge HE-3 (induced by source paper "the security reference architecture for blockchains towards a standardized model for studying vulnerabilities threats and defenses"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "a survey on ethereum systems security vulnerabilities attacks and defenses"
    - "security and privacy on blockchain"
    - "deconstructing blockchains a comprehensive survey on consensus membership and structure"
    - "truffle tests for free replaying ethereum smart contracts for transparency"
    - "blockchain technologies for the internet of things research issues and challenges"
    - "exploring the attack surface of blockchain a systematic overview"
    - "strongchain transparent and collaborative proof of work consensus"

Connected through hyperedge HE-4 (induced by source paper "targeted greybox fuzzing with static lookahead analysis"):
  with members:
    - "v fuzz vulnerability oriented evolutionary fuzzing"
    - "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"
    - "learning inputs in greybox fuzzing"

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)