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: "cost effective sampling for pairs of annotators"
  Abstract: Methods for automated collection and annotation are changing the cost-structures of random sampling surveys for a wide range of applications. Digital samples in the form of images, audio recordings or electronic documents can be collected cheaply, and in addition computer programs or crowd workers can be utilized to provide cheap annotations of collected samples. We consider the problem of estimating a population mean using random sampling under these new cost-structures and propose a novel `hybrid' sampling design. This design utilizes a pair of annotators, a primary, which is accurate but costly (e.g. a human expert) and an auxiliary which is noisy but cheap (e.g. a computer program), in order to minimize the total cost of collection and annotation. We show that hybrid sampling is applicable under a key condition: that the noise of the auxiliary annotator is smaller than the variance of the sampled data. Under this condition, hybrid sampling can reduce the amount of primary annotations needed and minimize total expenditures. The efficacy of hybrid sampling is demonstrated on two marine ecology data mining applications, where computer programs were utilized in a hybrid sampling designs to reduce the total cost by 50 - 79% compared to a sampling design that relied only on a human expert. In addition, a `transfer' sampling design is derived which use the auxiliary annotations only. Transfer sampling can be very cost-effective, but it requires a priori knowledge of the auxiliary annotator misclassification rates. We discuss specific situations where such design is applicable.

Connected through hyperedge HE-1 (induced by source paper "quantification in the wild data sets and baselines"):
  with members:
    - "simultaneous deep transfer across domains and tasks"
    - "whoi plankton a large scale fine grained visual recognition benchmark dataset for plankton classification"
    - "random sampling in an age of automation minimizing expenditures through balanced collection and annotation"
    - "caffe convolutional architecture for fast feature embedding"
    - "domain adaptation for statistical classifiers"

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.CY(Computers and Society)