human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "task recommendation in crowdsourcing based on learning preferences and reliabilities"):
  with members:
    - "fair task allocation in crowdsourced delivery"
    - "sequential multi class labeling in crowdsourcing"
    - "risk averse multi armed bandit problems under mean variance measure"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "risk aversion in multi armed bandits"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-2 (induced by source paper "a permutation based model for crowd labeling optimal estimation and robustness"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"

Connected through hyperedge HE-3 (induced by source paper "double or nothing multiplicative incentive mechanisms for crowdsourcing"):
  with members:
    - "competitive analysis of the top k ranking problem"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "reliable crowdsourcing for multi class labeling using coding theory"

Question: Please tell me which class the center node 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)