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: "computer aided verification for mechanism design"
  Abstract: We explore techniques from computer-aided verification to construct formal proofs of incentive properties. Because formal proofs can be automatically checked, agents do not need to manually check the properties, or even understand the proof. To demonstrate, we present the verification of a sophisticated mechanism: the generic reduction from Bayesian incentive compatible mechanism design to algorithm design given by Hartline, Kleinberg, and Malekian. This mechanism presents new challenges for formal verification, including essential use of randomness from both the execution of the mechanism and from the prior type distributions.

Connected through hyperedge HE-1 (induced by source paper "proving expected sensitivity of probabilistic programs"):
  with members:
    - "a semantic account of metric preservation"
    - "developing bug free machine learning systems with formal mathematics"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "liftings for differential privacy"
    - "coupling proofs are probabilistic product programs"
    - "probabilistic couplings for probabilistic reasoning"
    - "advanced probabilistic couplings for differential privacy"
    - "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"

Connected through hyperedge HE-2 (induced by source paper "proving differential privacy via probabilistic couplings"):
  with members:
    - "understanding the sparse vector technique for differential privacy"
    - "relational reasoning via probabilistic coupling"
    - "on the privacy properties of variants on the sparse vector technique"
    - "higher order approximate relational refinement types for mechanism design and differential privacy"
    - "proving differential privacy in hoare logic"
    - "preserving statistical validity in adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"
    - "logical metric and algorithmic characterisations of probabilistic bisimulation"

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)