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: "snorkel rapid training data creation with weak supervision"
  Abstract: Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8X faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8X speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.

Connected through hyperedge HE-1 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records"):
  with members:
    - "secnlp a survey of embeddings in clinical natural language processing"
    - "bpe dropout simple and effective subword regularization"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "named entity recognition in electronic health records using transfer learning bootstrapped neural networks"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "not enough data deep learning to the rescue"
    - "huggingface s transformers state of the art natural language processing"

Connected through hyperedge HE-2 (induced by source paper "factorized graph representations for semi supervised learning from sparse data"):
  with members:
    - "a general framework for estimating graphlet statistics via random walk"
    - "semi supervised classification with graph convolutional networks"
    - "graph based semi supervised learning for relational networks"
    - "semi supervised learning with heterophily"
    - "localization and centrality in networks"
    - "linearized and single pass belief propagation"
    - "spectral redemption in clustering sparse networks"
    - "image labeling on a network using social network metadata for image classification"

Connected through hyperedge HE-3 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors"):
  with members:
    - "cross modal data programming enables rapid medical machine learning"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "deep co training for semi supervised image recognition"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "inferring generative model structure with static analysis"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "learning the structure of generative models without labeled data"

Connected through hyperedge HE-4 (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 survey on data collection for machine learning a big data ai integration perspective"
    - "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"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "goggles automatic image labeling with affinity coding"):
  with members:
    - "a closer look at few shot classification"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "holoclean holistic data repairs with probabilistic inference"
    - "fusing data with correlations"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-6 (induced by source paper "theoretical model and practical considerations for data lineage reconstruction"):
  with members:
    - "towards federated learning at scale system design"
    - "differential privacy and machine learning a survey and review"

Connected through hyperedge HE-7 (induced by source paper "iterative data programming for expanding text classification corpora"):
  with members:
    - "bootstrapping conversational agents with weak supervision"
    - "document classification using expectation maximization with semi supervised learning"

Connected through hyperedge HE-8 (induced by source paper "xlnet generalized autoregressive pretraining for language understanding"):
  with members:
    - "improving question answering with external knowledge"
    - "option comparison network for multiple choice reading comprehension"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "unsupervised data augmentation for consistency training"
    - "roberta a robustly optimized bert pretraining approach"

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)