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 "certified computation in crowdsourcing"):
  Abstract: A wide range of learning tasks require human input in labeling massive data. For this labeling and because of the quantity of the data, crowdsourcing has become very crucial. The collected data though are usually low quality as workers are not appropriately incentivized to put effort. Significant research has focused on designing appropriate incentive schemes but do not capture the full range of tasks that appear in practice. Moreover, even if incentives are theoretically aligned noise in the data can still exist because it is hard to model exactly how workers will behave. #R##N#In this work, we provide a generic approach that is based on \textit{verification} of only few worker reports to guarantee high quality learning outcomes for various optimization objectives. Our method, identifies small sets of critical workers and verifies their reports. We show that many problems only need $poly(1/\varepsilon)$ verifications, to ensure that the output of the computation is at most a factor of $(1 \pm \varepsilon)$ away from the truth. For any given instance, we provide an \textit{instance optimal} solution that verifies the minimum number of workers possible to approximately certify correctness. #R##N#In case this certification step fails, a misreporting worker will be identified. Removing these workers and repeating until success, guarantees that the result will be correct and will depend only on the verified workers. Surprisingly, as we show, for several computation tasks more efficient methods are possible. These methods always guarantee that the produced result is not affected by the misreporting workers, since any misreport that affects the output will be detected and verified.

Includes members:
  Member "being robust in high dimensions can be practical":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private mean estimation of heavy tailed distributions")
  Member "learning from untrusted data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "list decodable subspace recovery")
  Member "feature multi selection among subjective features":
    also connected through other hyperedges:
      (none)
  Member "crowdsourced judgement elicitation with endogenous proficiency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "surrogate scoring rules")
  Member "mechanism design for time critical and cost critical task execution via crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "it s about time online macrotask sequencing in expert crowdsourcing")
  Member "optimal crowdsourcing contests":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "collaborative data acquisition")
  Member "the labor economics of paid crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "turkscanner predicting the hourly wage of microtasks")

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)