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: "squirrl automating attack discovery on blockchain incentive mechanisms with deep reinforcement learning"
  Abstract: Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing incentive-compatible incentive mechanisms is notoriously challenging, however. Even systems with strong theoretical security guarantees in traditional settings, where users are either Byzantine or honest, often exclude analysis of rational users, who may exploit incentives to deviate from honest behavior. As a result, most public blockchains today use incentive mechanisms whose security properties are poorly understood and largely untested. In this work, we propose SquirRL, a framework for using deep reinforcement learning to identify attack strategies on blockchain incentive mechanisms. With minimal setup, SquirRL replicates known theoretical results on the Bitcoin protocol. In more complex and realistic settings, as when mining power varies over time, it identifies attack strategies superior to those known in the literature. Finally, SquirRL yields results suggesting that classical selfish mining attacks against Bitcoin lose effectiveness in the presence of multiple attackers. These results shed light on why selfish mining, which is unobserved to date in the wild, may be a poor attack strategy.

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 "coalition safe equilibria with virtual payoffs"):
  with members:
    - "presto a systematic framework for blockchain consensus protocols"
    - "the gap game"
    - "blockchain mining games"
    - "optimal selfish mining strategies in bitcoin"
    - "majority is not enough bitcoin mining is vulnerable"
    - "on bitcoin and red balloons"
    - "an almost surely terminating polynomial protocol for asynchronous byzantine agreement with optimal resilience"

Connected through hyperedge HE-3 (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"
    - "an axiomatic approach to block rewards"
    - "reward sharing schemes for stake pools"
    - "compounding of wealth in proof of stake cryptocurrencies"
    - "blockmania from block dags to consensus"

Connected through hyperedge HE-4 (induced by source paper "heb hybrid expenditure blockchain"):
  with members:
    - "mind the mining"
    - "an axiomatic approach to block rewards"
    - "sbft a scalable and decentralized trust infrastructure"
    - "sbft a scalable decentralized trust infrastructure for blockchains"
    - "a survey on security and privacy issues of bitcoin"
    - "the gap game"
    - "bitcoin a natural oligopoly"
    - "be selfish and avoid dilemmas fork after withholding faw attacks on bitcoin"

Connected through hyperedge HE-5 (induced by source paper "hotpow finality from proof of work quorums"):
  with members:
    - "all that glitters is not bitcoin unveiling the centralized nature of the btc ip network"
    - "the consensus number of a cryptocurrency"
    - "txprobe discovering bitcoin s network topology using orphan transactions"
    - "consensus in the age of blockchains"
    - "bobtail a proof of work target that minimizes blockchain mining variance draft"
    - "hijacking bitcoin routing attacks on cryptocurrencies"
    - "blockchain mining games"
    - "enhancing bitcoin security and performance with strong consistency via collective signing"

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)