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: "redesigning bitcoin s fee market"
  Abstract: The security of the Bitcoin system is based on having a large amount of computational power in the hands of honest miners. Such miners are incentivized to join the system and validate transactions by the payments issued by the protocol to anyone who creates blocks. As new bitcoins creation rate decreases (halving every 4 years), the revenue derived from transaction fees start to have an increasingly important role. We argue that Bitcoin's current fee market does not extract revenue well when blocks are not congested. This effect has implications for the scalability debate: revenue from transaction fees may decrease if block size is increased. #R##N#The current mechanism is a "pay your bid" auction in which included transactions pay the amount they suggested. We propose two alternative auction mechanisms: The Monopolistic Price Mechanism, and the Random Sampling Optimal Price Mechanism (due to Goldberg et al.). In the monopolistic price mechanism, the miner chooses the number of accepted transactions in the block, and all transactions pay exactly the smallest bid included in the block. The mechanism thus sets the block size dynamically (up to a bound required for fast block propagation and other security concerns). We show, using analysis and simulations, that this mechanism extracts revenue better from users, and that it is nearly incentive compatible: the profit due to strategic bidding relative to honest biding decreases as the number of bidders grows. Users can then simply set their bids truthfully to exactly the amount they are willing to pay to transact, and do not need to utilize fee estimate mechanisms, do not resort to bid shading and do not need to adjust transaction fees (via replace-by-fee mechanisms) if the mempool grows. #R##N#We discuss these and other properties of our mechanisms, and explore various desired properties of fee market mechanisms for crypto-currencies.

Connected through hyperedge HE-1 (induced by source paper "sok tools for game theoretic models of security for cryptocurrencies"):
  with members:
    - "oceanic games centralization risks and incentives in blockchain mining"
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "selfish mining in ethereum"
    - "impossibility of full decentralization in permissionless blockchains"
    - "squirrl automating attack discovery on blockchain incentive mechanisms with deep reinforcement learning"
    - "an axiomatic approach to block rewards"
    - "reward sharing schemes for stake pools"
    - "compounding of wealth in proof of stake cryptocurrencies"

Connected through hyperedge HE-2 (induced by source paper "towards a functional fee market for cryptocurrencies"):
  with members:
    - "an incentive analysis of some bitcoin fee designs"
    - "majority is not enough bitcoin mining is vulnerable"
    - "analysis of bitcoin pooled mining reward systems"

Connected through hyperedge HE-3 (induced by source paper "the gap game"):
  with members:
    - "sustained space complexity"
    - "be selfish and avoid dilemmas fork after withholding faw attacks on bitcoin"
    - "solida a blockchain protocol based on reconfigurable byzantine consensus"
    - "optimal selfish mining strategies in bitcoin"
    - "bitcoin ng a scalable blockchain protocol"
    - "the miner s dilemma"
    - "majority is not enough bitcoin mining is vulnerable"
    - "on bitcoin and red balloons"

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)