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: "low cost learning via active data procurement"
  Abstract: We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. We study a model in which agents cannot fabricate data, but may lie about their cost of furnishing their data. The challenge is to use past data to actively price future data in order to obtain learning guarantees, even when agents' costs can depend arbitrarily on the data itself. We show how to convert a large class of no-regret algorithms into online posted-price and learning mechanisms. Our results parallel classic sample complexity guarantees, but with the key resource constraint being money rather than quantity of data available. With a budget constraint B, we give robust risk (predictive error) bounds on the order of 1/√B. In many cases our guarantees are significantly better due to an active-learning approach that leverages correlations between costs and data. Our algorithms and analysis go through a model of no-regret learning with T arriving pairs (cost, data) and a budget constraint of B, coupled with the "online to batch conversion". Our regret bounds for this model are on the order of T/√B and we give lower bounds on the same order.

Connected through hyperedge HE-1 (induced by source paper "towards data auctions with externalities"):
  with members:
    - "a marketplace for data an algorithmic solution"
    - "budget feasible mechanisms for experimental design"
    - "conducting truthful surveys cheaply"
    - "selling privacy at auction"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-2 (induced by source paper "what is the value of data on mathematical methods for data quality estimation"):
  with members:
    - "optimal data acquisition for statistical estimation"
    - "diameter based active learning"
    - "analysis of boolean functions"

Connected through hyperedge HE-3 (induced by source paper "prior free data acquisition for accurate statistical estimation"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"
    - "optimal data acquisition for statistical estimation"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "truthful linear regression"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"

Connected through hyperedge HE-4 (induced by source paper "mapping the potential and pitfalls of data dividends as a means of sharing the profits of artificial intelligence"):
  with members:
    - "measuring the importance of user generated content to search engines"
    - "towards efficient data valuation based on the shapley value"
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "incentivizing high quality crowdwork"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-5 (induced by source paper "incentive compatible active learning"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "learning time dependent choice"
    - "learning economic parameters from revealed preferences"
    - "efficiently learning from revealed preference"
    - "prediction markets mechanism design and cooperative game theory"

Connected through hyperedge HE-6 (induced by source paper "optimal data acquisition for statistical estimation"):
  with members:
    - "strategic classification from revealed preferences"
    - "truthful linear regression"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "buying private data without verification"
    - "conducting truthful surveys cheaply"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "selling privacy at auction"

Connected through hyperedge HE-7 (induced by source paper "parametric prediction from parametric agents"):
  with members:
    - "prediction markets mechanism design and cooperative game theory"

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)