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 "a survey on data collection for machine learning a big data ai integration perspective"):
  Abstract: Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning where feature engineering is the bottleneck, deep learning techniques automatically generate features, but instead require large amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research.

Includes members:
  Member "data cleaning for accurate fair and robust models a big data ai integration approach":
    also connected through other hyperedges:
      (none)
  Member "the gan landscape losses architectures regularization and normalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "adversarial example generation with syntactically controlled paraphrase networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "snorkel drybell a case study in deploying weak supervision at industrial scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "slice tuner a selective data collection framework for accurate and fair machine learning models")
  Member "data synthesis based on generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentially private mean embeddings with random features dp merf for simple practical synthetic data generation")
  Member "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep robust subjective visual property prediction in crowdsourcing")
  Member "a survey on deep transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "learning based video motion magnification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "extended local binary patterns for efficient and robust spontaneous facial micro expression recognition")

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.LG(Machine Learning)