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: "random sampling in an age of automation minimizing expenditures through balanced collection and annotation"
  Abstract: Methods for automated collection and annotation are changing the cost-structures of sampling surveys for a wide range of applications. Digital samples in the form of images or audio recordings can be collected rapidly, and annotated by computer programs or crowd workers. We consider the problem of estimating a population mean under these new cost-structures, and propose a Hybrid-Offset sampling design. This design utilizes two 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 total sampling expenditures. Our analysis gives necessary conditions for the Hybrid-Offset design and specifies optimal sample sizes for both annotators. Simulations on data from a coral reef survey program indicate that the Hybrid-Offset design outperforms several alternative sampling designs. In particular, sampling expenditures are reduced 50% compared to the Conventional design currently deployed by the coral ecologists.

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"
    - "cost effective sampling for pairs of annotators"
    - "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)