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 "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"):
  Abstract: We consider a crowdsourcing model in which $n$ workers are asked to rate the quality of $n$ items previously generated by other workers. An unknown set of $\alpha n$ workers generate reliable ratings, while the remaining workers may behave arbitrarily and possibly adversarially. The manager of the experiment can also manually evaluate the quality of a small number of items, and wishes to curate together almost all of the high-quality items with at most an $\epsilon$ fraction of low-quality items. Perhaps surprisingly, we show that this is possible with an amount of work required of the manager, and each worker, that does not scale with $n$: the dataset can be curated with $\tilde{O}\Big(\frac{1}{\beta\alpha^3\epsilon^4}\Big)$ ratings per worker, and $\tilde{O}\Big(\frac{1}{\beta\epsilon^2}\Big)$ ratings by the manager, where $\beta$ is the fraction of high-quality items. Our results extend to the more general setting of peer prediction, including peer grading in online classrooms.

Includes members:
  Member "multisection in the stochastic block model using semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "robust collaborative online learning":
    also connected through other hyperedges:
      (none)
  Member "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "randomized spectral clustering in large scale stochastic block models")
  Member "truth serums for massively crowdsourced evaluation tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dominantly truthful multi task peer prediction with a constant number of tasks")
  Member "how robust are reconstruction thresholds for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improper learning for non stochastic control")
  Member "approval voting and incentives in crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins")
  Member "provably manipulation resistant reputation systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "collaborative prediction with expert advice")
  Member "double or nothing multiplicative incentive mechanisms for crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers")

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.HC(Human-Computer Interaction)