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: "faithful implementations of distributed algorithms and control laws"
  Abstract: When a distributed algorithm must be executed by strategic agents with misaligned interests, a social leader needs to introduce an appropriate tax/subsidy mechanism to incentivize agents to faithfully implement the intended algorithm so that a correct outcome is obtained. We discuss the incentive issues of implementing economically efficient distributed algorithms using the framework of indirect mechanism design theory. In particular, we show that indirect Groves mechanisms are not only sufficient but also necessary to achieve incentive compatibility. This result can be viewed as a generalization of the Green-Laffont theorem to indirect mechanisms. Then, we introduce the notion of asymptotic incentive compatibility as an appropriate solution concept to faithfully implement distributed and iterative optimization algorithms. We consider two special types of optimization algorithms: dual decomposition algorithms for resource allocation and average consensus algorithms.

Connected through hyperedge HE-1 (induced by source paper "predicting performance of asynchronous differentially private learning"):
  with members:
    - "the value of collaboration in convex machine learning with differential privacy"
    - "dp admm admm based distributed learning with differential privacy"
    - "privacy preserving machine learning through data obfuscation"
    - "learning differentially private recurrent language models"
    - "asynchronous methods for deep reinforcement learning"
    - "on the differential privacy of bayesian inference"
    - "functional mechanism regression analysis under differential privacy"

Connected through hyperedge HE-2 (induced by source paper "incentive design in a distributed problem with strategic agents"):
  with members:
    - "a robust utility learning framework via inverse optimization"
    - "game theoretic multi agent control and network cost allocation under communication constraints"
    - "distributed multi step power scheduling and cost allocation for cooperative microgrids"

Connected through hyperedge HE-3 (induced by source paper "coalitional game with opinion exchange"):
  with members:
    - "simultaneous velocity and position estimation via distance only measurements with application to multi agent system control"
    - "coalition formation games for distributed cooperation among roadside units in vehicular networks"

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)