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 drybell a case study in deploying weak supervision at industrial scale"
  Abstract: Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting. Snorkel DryBell builds on the Snorkel framework, extending it in three critical aspects: flexible, template-based ingestion of diverse organizational knowledge, cross-feature production serving, and scalable, sampling-free execution. On three classification tasks at Google, we find that Snorkel DryBell creates classifiers of comparable quality to ones trained with tens of thousands of hand-labeled examples, converts non-servable organizational resources to servable models for an average 52% performance improvement, and executes over millions of data points in tens of minutes.

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"
    - "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"
    - "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 "superglue a stickier benchmark for general purpose language understanding systems"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "paws paraphrase adversaries from word scrambling"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"

Connected through hyperedge HE-3 (induced by source paper "slice based learning a programming model for residual learning in critical data slices"):
  with members:
    - "overton a data system for monitoring and improving machine learned products"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural network acceptability judgments"

Connected through hyperedge HE-4 (induced by source paper "overton a data system for monitoring and improving machine learned products"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "slice based learning a programming model for residual learning in critical data slices"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "autoaugment learning augmentation policies from data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snorkel rapid training data creation with weak supervision"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "stochastic generalized adversarial label learning"):
  with members:
    - "adversarial label learning"
    - "unsupervised risk estimation using only conditional independence structure"
    - "unsupervised ensemble learning with dependent classifiers"
    - "optimally combining classifiers using unlabeled data"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "scalable semi supervised aggregation of classifiers"

Connected through hyperedge HE-6 (induced by source paper "almawave slu a new dataset for slu in italian"):
  with members:
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a survey on data collection for machine learning a big data ai integration perspective"):
  with members:
    - "data cleaning for accurate fair and robust models a big data ai integration approach"
    - "the gan landscape losses architectures regularization and normalization"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "data synthesis based on generative adversarial networks"
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "a survey on deep transfer learning"
    - "learning based video motion magnification"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-8 (induced by source paper "snorkel rapid training data creation with weak supervision"):
  with members:
    - "fonduer knowledge base construction from richly formatted data"
    - "training classifiers with natural language explanations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "inferring generative model structure with static analysis"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning the structure of generative models without labeled data"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "tensorflow a system for large scale machine learning"

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)