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: "optimal mechanisms for selling information"
  Abstract: The buying and selling of information is taking place at a scale unprecedented in the history of commerce, thanks to the formation of online marketplaces for user data. Data providing agencies sell user information to advertisers to allow them to match ads to viewers more effectively. In this paper we study the design of optimal mechanisms for a monopolistic data provider to sell information to a buyer, in a model where both parties have (possibly correlated) private signals about a state of the world, and the buyer uses information learned from the seller, along with his own signal, to choose an action (e.g., displaying an ad) whose payoff depends on the state of the world. #R##N#We provide sufficient conditions under which there is a simple one-round protocol (i.e. a protocol where the buyer and seller each sends a single message, and there is a single money transfer) achieving optimal revenue. In these cases we present a polynomial-time algorithm that computes the optimal mechanism. Intriguingly, we show that multiple rounds of partial information disclosure (interleaved by payment to the seller) are sometimes necessary to achieve optimal revenue if the buyer is allowed to abort his interaction with the seller prematurely. We also prove some negative results about the inability of simple mechanisms for selling information to approximate more complicated ones in the worst case.

Connected through hyperedge HE-1 (induced by source paper "computing the value of spatio temporal data in wholesale and retail data marketplaces"):
  with members:
    - "valuating user data in a human centric data economy"
    - "computing shapley values in the plane"
    - "a marketplace for data an algorithmic solution"
    - "the cost of stability in coalitional games"

Connected through hyperedge HE-2 (induced by source paper "revenue maximization for query pricing"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "the design of arbitrage free data pricing schemes"
    - "simple mechanisms for subadditive buyers via duality"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "independent set induced matching and pricing connections and tight subexponential time approximation hardnesses"
    - "the power of randomness in bayesian optimal mechanism design"

Connected through hyperedge HE-3 (induced by source paper "competitive statistical estimation with strategic data sources"):
  with members:
    - "optimal data acquisition for statistical estimation"
    - "statistical estimation with strategic data sources in competitive settings"
    - "informed truthfulness in multi task peer prediction"
    - "promoting truthful behavior in participatory sensing mechanisms"
    - "estimation with strategic sensors"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-4 (induced by source paper "combinatorial auctions with interdependent valuations sos to the rescue"):
  with members:
    - "interdependent values without single crossing"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "approximate revenue maximization in interdependent value settings"
    - "optimal auctions with correlated bidders are easy"

Connected through hyperedge HE-5 (induced by source paper "selling information through consulting"):
  with members:
    - "targeting and signaling in ad auctions"
    - "algorithmic bayesian persuasion"
    - "send mixed signals earn more work less"

Connected through hyperedge HE-6 (induced by source paper "a marketplace for data an algorithmic solution"):
  with members:
    - "introduction to online convex optimization"
    - "adversarially robust generalization requires more data"
    - "statistical cost sharing"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"
    - "learning in auctions regret is hard envy is easy"
    - "bounding the estimation error of sampling based shapley value approximation with without stratifying"

Connected through hyperedge HE-7 (induced by source paper "obtaining costly unverifiable valuations from a single agent"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"

Connected through hyperedge HE-8 (induced by source paper "sequential posted price mechanisms with correlated valuations"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "online contention resolution schemes"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "approximate revenue maximization in interdependent value settings"
    - "combinatorial auctions via posted prices"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"

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)