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: "mechanism design for data science"
  Abstract: Good economic mechanisms depend on the preferences of participants in the mechanism. For example, the revenue-optimal auction for selling an item is parameterized by a reserve price, and the appropriate reserve price depends on how much the bidders are willing to pay. A mechanism designer can potentially learn about the participants' preferences by observing historical data from the mechanism; the designer could then update the mechanism in response to learned preferences to improve its performance. The challenge of such an approach is that the data corresponds to the actions of the participants and not their preferences. Preferences can potentially be inferred from actions but the degree of inference possible depends on the mechanism. In the optimal auction example, it is impossible to learn anything about preferences of bidders who are not willing to pay the reserve price. These bidders will not cast bids in the auction and, from historical bid data, the auctioneer could never learn that lowering the reserve price would give a higher revenue (even if it would). To address this impossibility, the auctioneer could sacrifice revenue optimality in the initial auction to obtain better inference properties so that the auction's parameters can be adapted to changing preferences in the future. This paper develops the theory for optimal mechanism design subject to good inferability.

Connected through hyperedge HE-1 (induced by source paper "estimating approximate incentive compatibility"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "optimal auctions through deep learning"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "a b testing of auctions"
    - "learning simple auctions"

Connected through hyperedge HE-2 (induced by source paper "online learning for measuring incentive compatibility in ad auctions"):
  with members:
    - "learning to bid without knowing your value"
    - "multiplicative pacing equilibria in auction markets"
    - "combinatorial multi armed bandit with general reward functions"
    - "online learning in repeated auctions"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "online learning and profit maximization from revealed preferences"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "response prediction for low regret agents"):
  with members:
    - "selling to a no regret buyer"
    - "econometrics for learning agents"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "inference from auction prices"):
  with members:
    - "a b testing of auctions"
    - "econometrics for learning agents"

Connected through hyperedge HE-5 (induced by source paper "an end to end argument in mechanism design prior independent auctions for budgeted agents"):
  with members:
    - "a b testing of auctions"
    - "the price of anarchy in auctions"
    - "randomization beats second price as a prior independent auction"
    - "approximate revenue maximization in interdependent value settings"
    - "simple and near optimal mechanisms for market intermediation"
    - "price of anarchy for auction revenue"
    - "efficiency guarantees in auctions with budgets"
    - "prior independent mechanisms for scheduling"

Connected through hyperedge HE-6 (induced by source paper "a general theory of sample complexity for multi item profit maximization"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "submultiplicative glivenko cantelli and uniform convergence of revenues"
    - "learning multi item auctions with or without samples"
    - "revenue optimization with approximate bid predictions"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "do prices coordinate markets"

Connected through hyperedge HE-7 (induced by source paper "learning to bid without knowing your value"):
  with members:
    - "bandits with movement costs and adaptive pricing"
    - "revenue optimization with approximate bid predictions"
    - "online learning with feedback graphs without the graphs"
    - "the sample complexity of revenue maximization"
    - "learning and efficiency in games with dynamic population"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"
    - "online learning and profit maximization from revealed preferences"

Connected through hyperedge HE-8 (induced by source paper "statistical cost sharing"):
  with members:
    - "the sample complexity of revenue maximization"
    - "learning cooperative games"
    - "the limitations of optimization from samples"
    - "sampling and representation complexity of revenue maximization"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "intriguing properties of neural networks"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "learning coverage functions and private release of marginals"

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)