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: "the truth behind the myth of the folk theorem"
  Abstract: We study the problem of computing an $\epsilon$-Nash equilibrium in repeated games. Earlier work by Borgs et al. [2010] suggests that this problem is intractable. We show that if we make a slight change to their model---modeling the players as polynomial-time Turing machines that maintain state ---and make some standard cryptographic hardness assumptions (the existence of public-key encryption), the problem can actually be solved in polynomial time. Our algorithm works not only for games with a finite number of players, but also for constant-degree graphical games. #R##N#As Nash equilibrium is a weak solution concept for extensive form games, we additionally define and study an appropriate notion of a subgame-perfect equilibrium for computationally bounded players, and show how to efficiently find such an equilibrium in repeated games (again, making standard cryptographic hardness assumptions).

Connected through hyperedge HE-1 (induced by source paper "on the non existence of nash equilibrium in games with resource bounded players"):
  with members:
    - "computational extensive form games"
    - "algorithmic rationality game theory with costly computation"
    - "on subversive miner strategies and block withholding attack in bitcoin digital currency"

Connected through hyperedge HE-2 (induced by source paper "computational extensive form games"):
  with members:
    - "cryptographically blinded games leveraging players limitations for equilibria and profit"
    - "sequential rationality in cryptographic protocols"

Connected through hyperedge HE-3 (induced by source paper "an anti folk theorem for large repeated games with imperfect monitoring"):
  with members:
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "mechanism design in large games incentives and privacy"
    - "beating randomized response on incoherent matrices"
    - "truthful mechanisms for agents that value privacy"
    - "privacy aware mechanism design"
    - "approximately optimal mechanism design via differential privacy"

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)