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 "reliable crowdsourcing under the generalized dawid skene model"):
  Abstract: Crowdsourcing systems provide scalable and cost-effective human-powered solutions at marginal cost, for classification tasks where humans are significantly better than the machines. Although traditional approaches in aggregating crowdsourced labels have relied on the Dawid-Skene model, this fails to capture how some tasks are inherently more difficult than the others. Several generalizations have been proposed, but inference becomes intractable and typical solutions resort to heuristics. To bridge this gap, we study a recently proposed generalize Dawid-Skene model, and propose a linear-time algorithm based on spectral methods. We show near-optimality of the proposed approach, by providing an upper bound on the error and comparing it to a fundamental limit. We provide numerical experiments on synthetic data matching our analyses, and also on real datasets demonstrating that the spectral method significantly improves over simple majority voting and is comparable to other methods.

Includes members:
  Member "crowdsourcing with sparsely interacting workers":
    also connected through other hyperedges:
      (none)
  Member "embeddings of schatten norms with applications to data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "spectrum estimation from samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "a permutation based model for crowd labeling optimal estimation and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "doubly robust crowdsourcing")
  Member "regularized minimax conditional entropy for crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collaborative data acquisition")
  Member "spectral redemption in clustering sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "budget optimal task allocation for reliable crowdsourcing systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crowdsourced classification with xor queries fundamental limits and an efficient algorithm")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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.LG(Machine Learning)