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: "impossibility of full decentralization in permissionless blockchains"
  Abstract: Bitcoin uses blockchain technology and proof-of-work (PoW) mechanism where nodes spend computing resources and earn rewards in return for spending these resources. This incentive system has caused power to be significantly biased towards a few nodes, called mining pools. In fact, poor decentralization appears not only in PoW-based coins but also in coins adopting other mechanisms such as proof-of-stake (PoS) and delegated proof-of-stake (DPoS). In this paper, we target this centralization issue. To this end, we first define (m, \varepsilon, \delta)-decentralization as a state that satisfies 1) there are at least m participants running a node and 2) the ratio between the total resource power of nodes run by the richest and \delta-th percentile participants is less than or equal to 1+\varepsilon. To see if it is possible to achieve good decentralization, we introduce sufficient conditions for the incentive system of a blockchain to reach (m, \varepsilon, \delta)-decentralization. Then we find an incentive system satisfying these conditions. Through this incentive system, a blockchain system can reach full decentralization with probability 1. However, to adopt this incentive system, the blockchain system should be able to assign a positive Sybil cost, where the Sybil cost is defined as the difference between the cost for one participant running multiple nodes and the total cost for multiple participants each running one node. On the other hand, we prove that when there is no Sybil cost, the probability of reaching (m, \varepsilon, \delta)-decentralization is upper bounded by a value close to 0, considering a large rich-poor gap. To determine the conditions that each system cannot satisfy, we also analyze protocols of all PoW, PoS, and DPoS coins in the top 100 coins according to our conditions. Finally, we conduct data analysis of these coins to validate our theory.

Connected through hyperedge HE-1 (induced by source paper "comparison of decentralization in dpos and pow blockchains"):
  with members:
    - "incentivized blockchain based social media platforms a case study of steemit"
    - "decentralization in bitcoin and ethereum networks"
    - "popularity and quality in social news aggregators a study of reddit and hacker news"
    - "the miner s dilemma"
    - "majority is not enough bitcoin mining is vulnerable"

Connected through hyperedge HE-2 (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"
    - "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"
    - "blockmania from block dags to consensus"

Connected through hyperedge HE-3 (induced by source paper "zero knowledge proof of identity sybil resistant anonymous authentication on permissionless blockchains and incentive compatible strictly dominant cryptocurrencies"):
  with members:
    - "diversification across mining pools optimal mining strategies under pow"
    - "sok tools for game theoretic models of security for cryptocurrencies"
    - "dfinity technology overview series consensus system"
    - "a game theoretic analysis of shard based permissionless blockchains"
    - "coconut threshold issuance selective disclosure credentials with applications to distributed ledgers"
    - "be selfish and avoid dilemmas fork after withholding faw attacks on bitcoin"
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "keeping authorities honest or bust with decentralized witness cosigning"

Connected through hyperedge HE-4 (induced by source paper "reward sharing schemes for stake pools"):
  with members:
    - "a deep dive into bitcoin mining pools an empirical analysis of mining shares"
    - "cryptocurrency egalitarianism a quantitative approach"
    - "an axiomatic approach to block rewards"
    - "compounding of wealth in proof of stake cryptocurrencies"
    - "equilibrium computation in atomic splittable routing games with convex cost functions"
    - "bitcoin a natural oligopoly"
    - "socially optimal mining pools"
    - "blockchain mining games"

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)