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: "learning valuation distributions from partial observation"
  Abstract: Auction theory traditionally assumes that bidders' valuation distributions are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson auction. However, this theory does not describe how the auctioneer comes to possess this information. Recently, Cole and Roughgarden [2014] showed that an approximation based on a finite sample of independent draws from each bidder's distribution is sufficient to produce a near-optimal auction. In this work, we consider the problem of learning bidders' valuation distributions from much weaker forms of observations. Specifically, we consider a setting where there is a repeated, sealed-bid auction with $n$ bidders, but all we observe for each round is who won, but not how much they bid or paid. We can also participate (i.e., submit a bid) ourselves, and observe when we win. From this information, our goal is to (approximately) recover the inherently recoverable part of the underlying bid distributions. We also consider extensions where different subsets of bidders participate in each round, and where bidders' valuations have a common-value component added to their independent private values.

Connected through hyperedge HE-1 (induced by source paper "heavy tails make happy buyers"):
  with members:
    - "the sample complexity of revenue maximization"
    - "incorporating hidden costs of annoying ads in display auctions"

Connected through hyperedge HE-2 (induced by source paper "explicit shading strategies for repeated truthful auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "how to manipulate truthful prior dependent mechanisms"
    - "the sample complexity of revenue maximization"
    - "online learning in repeated auctions"
    - "learning in auctions regret is hard envy is easy"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-3 (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-4 (induced by source paper "online learning in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "the sample complexity of revenue maximization"
    - "adaptive online learning"
    - "optimal auctions for correlated buyers with sampling"
    - "online learning and profit maximization from revealed preferences"
    - "mechanism design for data science"
    - "bandits with knapsacks"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-5 (induced by source paper "watch and learn optimizing from revealed preferences feedback"):
  with members:
    - "the sample complexity of revenue maximization"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "the pseudo dimension of near optimal auctions"
    - "learning economic parameters from revealed preferences"
    - "sampling and representation complexity of revenue maximization"
    - "achieving target equilibria in network routing games without knowing the latency functions"
    - "online learning and profit maximization from revealed preferences"
    - "making the most of your samples"

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)