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: "a stochastic game framework for analyzing computational investment strategies in distributed computing with application to blockchain mining"
  Abstract: We study a stochastic game framework with varying number of players, for modeling and analyzing their computational investment strategies in distributed computing, for solving a problem such as in blockchain mining. In particular, we propose a continuous time Markov chain model, where players arrive and depart according to a stochastic process, and determine their investment strategies based on the state of the system. The players obtain a certain reward for being the first to solve the problem, while incur a certain cost based on the time and computational power invested in the attempt to solve it. We consider that the players are Markovian, that is, they determine their strategies which maximize their expected utilities, while ignoring past payoffs. We first study a scenario where the rate of problem getting solved is proportional to the total computational power invested by the players. We show that, in statewise Nash equilibrium, players with costs exceeding a certain threshold do not invest, while players with costs less than this threshold invest maximum power. Further, we show that Markov perfect equilibrium follows a similar threshold policy, and the players do not need to have knowledge of the system state. We then consider a scenario where the rate of problem getting solved is independent of the computational power invested by players. Here, we show that, in statewise Nash equilibrium, only the players with cost parameters in a relatively low range, invest. We also show that, in Markov perfect equilibrium, players invest proportionally to the reward-cost ratio. Using simulations, we quantify the effects of arrival and departure rates on players' expected utilities and provide insights

Connected through hyperedge HE-1 (induced by source paper "a survey on applications of game theory in blockchain"):
  with members:
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "security services using blockchains a state of the art survey"
    - "blockchain governance game"
    - "exchange of renewable energy among prosumers using blockchain with dynamic pricing"
    - "game of coins"
    - "delayed blockchain protocols"
    - "reward sharing schemes for stake pools"
    - "a survey on the security of blockchain systems"

Connected through hyperedge HE-2 (induced by source paper "presto a systematic framework for blockchain consensus protocols"):
  with members:
    - "oceanic games centralization risks and incentives in blockchain mining"
    - "weighted voting on the blockchain improving consensus in proof of stake protocols"
    - "blockchain trilemma solver algorand has dilemma over undecidable messages"
    - "blockchain technologies for the internet of things research issues and challenges"
    - "strongchain transparent and collaborative proof of work consensus"
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "sok tools for game theoretic models of security for cryptocurrencies"
    - "incentives in ethereum s hybrid casper protocol"

Connected through hyperedge HE-3 (induced by source paper "agent based simulations of blockchain protocols illustrated via kadena s chainweb"):
  with members:
    - "decentralization in bitcoin and ethereum networks"
    - "dfinity technology overview series consensus system"
    - "on profitability of selfish mining"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-4 (induced by source paper "on the origins and variations of blockchain technologies"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "blockchain technology overview"
    - "blockchains consensus protocols in the wild"

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.GT(Computer Science and Game Theory)