human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "budget feasible mechanisms for experimental design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal feature discovery through strategic modification")
  Member "buying private data without verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "causal feature discovery through strategic modification")
  Member "conducting truthful surveys cheaply":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards data auctions with externalities")
  Member "take it or leave it running a survey when privacy comes at a cost":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning for the manipulation of eye tracking data")
  Member "selling privacy at auction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "agnostic active learning without constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "importance weighted active learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive region based active learning")

Question: Please tell me which class the source paper 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)