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: "efficiency of active learning for the allocation of workers on crowdsourced classification tasks"
  Abstract: Crowdsourcing has been successfully employed in the past as an effective and cheap way to execute classification tasks and has therefore attracted the attention of the research community. However, we still lack a theoretical understanding of how to collect the labels from the crowd in an optimal way. In this paper we focus on the problem of worker allocation and compare two active learning policies proposed in the empirical literature with a uniform allocation of the available budget. To this end we make a thorough mathematical analysis of the problem and derive a new bound on the performance of the system. Furthermore we run extensive simulations in a more realistic scenario and show that our theoretical results hold in practice.

Connected through hyperedge HE-1 (induced by source paper "bayesian active learning with abstention feedbacks"):
  with members:
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "adaptive maximization of pointwise submodular functions with budget constraint"
    - "robustness of bayesian pool based active learning against prior misspecification"
    - "active learning from weak and strong labelers"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "noisy bayesian active learning"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "adversarial task assignment"):
  with members:
    - "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-3 (induced by source paper "adversarial task allocation"):
  with members:
    - "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"
    - "robust pca via outlier pursuit"

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.HC(Human-Computer Interaction)