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: "flash boys 2 0 frontrunning transaction reordering and consensus instability in decentralized exchanges"
  Abstract: Blockchains, and specifically smart contracts, have promised to create fair and transparent trading ecosystems. #R##N#Unfortunately, we show that this promise has not been met. We document and quantify the widespread and rising deployment of arbitrage bots in blockchain systems, specifically in decentralized exchanges (or "DEXes"). Like high-frequency traders on Wall Street, these bots exploit inefficiencies in DEXes, paying high transaction fees and optimizing network latency to frontrun, i.e., anticipate and exploit, ordinary users' DEX trades. #R##N#We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots. We also study bots' profit-making strategies, with a focus on blockchain-specific elements. We observe bots engage in what we call priority gas auctions (PGAs), competitively bidding up transaction fees in order to obtain priority ordering, i.e., early block position and execution, for their transactions. PGAs present an interesting and complex new continuous-time, partial-information, game-theoretic model that we formalize and study. We release an interactive web portal, this http URL, to provide the community with real-time data on PGAs. #R##N#We additionally show that high fees paid for priority transaction ordering poses a systemic risk to consensus-layer security. We explain that such fees are just one form of a general phenomenon in DEXes and beyond---what we call miner extractable value (MEV)---that poses concrete, measurable, consensus-layer security risks. We show empirically that MEV poses a realistic threat to Ethereum today. #R##N#Our work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "reliable federated learning for mobile networks"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"

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"
    - "harvey a greybox fuzzer for smart contracts"
    - "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 "squirrl automating attack discovery on blockchain incentive mechanisms with deep reinforcement learning"):
  with members:
    - "competing semi selfish miners in bitcoin"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "selfish mining in ethereum"
    - "mind the mining"
    - "selfish mining in ethereum"
    - "a deep dive into blockchain selfish mining"
    - "an introduction to deep reinforcement learning"
    - "scaling nakamoto consensus to thousands of transactions per second"

Connected through hyperedge HE-5 (induced by source paper "bdos blockchain denial of service"):
  with members:
    - "competing semi selfish miners in bitcoin"
    - "bitcoin vs bitcoin cash coexistence or downfall of bitcoin cash"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "selfish mining in ethereum"
    - "just enough security reducing proof of work ecological footprint"
    - "mind the mining"
    - "cryptocurrency egalitarianism a quantitative approach"
    - "a deep dive into blockchain selfish mining"

Connected through hyperedge HE-6 (induced by source paper "the operational cost of ethereum airdrops"):
  with members:
    - "detecting token systems on ethereum"
    - "airdrops and privacy a case study in cross blockchain analysis"
    - "an adaptive gas cost mechanism for ethereum to defend against under priced dos attacks"
    - "under optimized smart contracts devour your money"

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)