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: "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.

Connected through hyperedge HE-1 (induced by source paper "slice tuner a selective data collection framework for accurate and fair machine learning models"):
  with members:
    - "slice based learning a programming model for residual learning in critical data slices"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "fairgan fairness aware generative adversarial networks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "snorkel rapid training data creation with weak supervision"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "a review of machine learning applications in wildfire science and management"):
  with members:
    - "tackling climate change with machine learning"
    - "distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning"
    - "regularization for deep learning a taxonomy"

Connected through hyperedge HE-3 (induced by source paper "deep learning surrogate models for spatial and visual connectivity"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "training with the invisibles obfuscating images to share safely for learning visual recognition models"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to anonymize faces for privacy preserving action detection"
    - "on classification of distorted images with deep convolutional neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "toward multimodal image to image translation"

Connected through hyperedge HE-6 (induced by source paper "search based repair of deep neural networks"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "guiding deep learning system testing using surprise adequacy"
    - "arja automated repair of java programs via multi objective genetic programming"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "a survey on deep learning in medical image analysis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-7 (induced by source paper "dataset search a survey"):
  with members:
    - "interactive summarization and exploration of top aggregate query answers"
    - "ad hoc table retrieval using semantic similarity"
    - "cuttlefish a lightweight primitive for adaptive query processing"
    - "on the fly table generation"
    - "everything you always wanted to know about a dataset studies in data summarisation"
    - "neural vector spaces for unsupervised information retrieval"
    - "entitables smart assistance for entity focused tables"
    - "navigating the data lake with datamaran automatically extracting structure from log datasets"

Connected through hyperedge HE-8 (induced by source paper "a comparative study between bayesian and frequentist neural networks for remaining useful life estimation in condition based maintenance"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "temporal convolutional memory networks for remaining useful life estimation of industrial machinery"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semi supervised learning with deep generative models for asset failure prediction"
    - "predicting remaining useful life using time series embeddings based on recurrent neural networks"
    - "normalization a preprocessing stage"

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.LG(Machine Learning)