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 "goggles automatic training data generation with affinity coding"):
  Abstract: Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, data programming has been proposed in the data management community to reduce the human cost in training data generation. Data programming expects users to write a set of labeling functions, each of which is a weak supervision source that labels a subset of data points with better-than-random accuracy. However, the success of data programming heavily depends on the quality (in terms of both accuracy and coverage) of the labeling functions that users still need to design manually. #R##N#We propose affinity coding, a new paradigm for fully automatic generation of training data. In affinity coding, the similarity between the unlabeled instances and prototypes that are derived from the same unlabeled instances serve as signals (or sources of weak supervision) for determining class membership. We term this implicit similarity as the affinity score. Consequently, we can have as many sources of weak supervision as the number of unlabeled data points, without any human input. We also propose a system called GOGGLES that is an implementation of affinity coding for labeling image datasets. GOGGLES features novel techniques for deriving affinity scores from image datasets based on "semantic prototypes" extracted from convolutional neural nets, as well as an expectation-maximization approach for performing class label inference based on the computed affinity scores. #R##N#Compared to the state-of-the-art data programming system Snorkel, GOGGLES exhibits 14.88% average improvement in terms of the quality of labels generated for the binary labeling task. The GOGGLES system is open-sourced at this https URL.

Includes members:
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "snorkel rapid training data creation with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "holoclean holistic data repairs with probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "fusing data with correlations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "goggles automatic image labeling with affinity coding")
  Member "slimfast guaranteed results for data fusion and source reliability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "technical report optimizing human involvement for entity matching and consolidation")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "visualizing and understanding convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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.CV(Computer Vision and Pattern Recognition)