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 "information gathering with peers submodular optimization with peer prediction constraints"):
  Abstract: We study a problem of optimal information gathering from multiple data providers that need to be incentivized to provide accurate information. This problem arises in many real world applications that rely on crowdsourced data sets, but where the process of obtaining data is costly. A notable example of such a scenario is crowd sensing. To this end, we formulate the problem of optimal information gathering as maximization of a submodular function under a budget constraint, where the budget represents the total expected payment to data providers. Contrary to the existing approaches, we base our payments on incentives for accuracy and truthfulness, in particular, {\em peer-prediction} methods that score each of the selected data providers against its best peer, while ensuring that the minimum expected payment is above a given threshold. We first show that the problem at hand is hard to approximate within a constant factor that is not dependent on the properties of the payment function. However, for given topological and analytical properties of the instance, we construct two greedy algorithms, respectively called PPCGreedy and PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the optimal solution. Finally, we evaluate our methods using a realistic crowd sensing testbed.

Includes members:
  Member "incentivizing evaluation via limited access to ground truth peer prediction makes things worse":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "credible sample elicitation")
  Member "a framework for designing information elicitation mechanisms that reward truth telling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "informed truthfulness in multi task peer prediction")
  Member "learning to incentivize eliciting effort via output agreement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prior free data acquisition for accurate statistical estimation")
  Member "crowd access path optimization diversity matters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combining crowd and machines for multi predicate item screening")
  Member "information gathering in networks via active exploration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the fairness of time critical influence maximization in social networks")
  Member "incentives for privacy tradeoff in community sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "curvature and optimal algorithms for learning and minimizing submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")

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)