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 bayesian clearing mechanism for combinatorial auctions"
  Abstract: We cast the problem of combinatorial auction design in a Bayesian framework in order to incorporate prior information into the auction process and minimize the number of rounds to convergence. We first develop a generative model of agent valuations and market prices such that clearing prices become maximum a posteriori estimates given observed agent valuations. This generative model then forms the basis of an auction process which alternates between refining estimates of agent valuations and computing candidate clearing prices. We provide an implementation of the auction using assumed density filtering to estimate valuations and expectation maximization to compute prices. An empirical evaluation over a range of valuation domains demonstrates that our Bayesian auction mechanism is highly competitive against the combinatorial clock auction in terms of rounds to convergence, even under the most favorable choices of price increment for this baseline.

Connected through hyperedge HE-1 (induced by source paper "machine learning powered iterative combinatorial auctions"):
  with members:
    - "fast iterative combinatorial auctions via bayesian learning"
    - "a general theory of sample complexity for multi item profit maximization"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "the sample complexity of revenue maximization"
    - "ironing in the dark"
    - "complexity of mechanism design"

Connected through hyperedge HE-2 (induced by source paper "deep learning powered iterative combinatorial auctions"):
  with members:
    - "machine learning powered iterative combinatorial auctions"
    - "fast iterative combinatorial auctions via bayesian learning"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a bridge between liquid and social welfare in combinatorial auctions with submodular bidders"):
  with members:
    - "best response dynamics in combinatorial auctions with item bidding"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders"
    - "bayesian budget feasibility with posted pricing"
    - "combinatorial auctions via posted prices"
    - "budget feasible mechanism design from prior free to bayesian"
    - "from convex optimization to randomized mechanisms toward optimal combinatorial auctions"
    - "on the approximability of budget feasible mechanisms"

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)