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: "holoclean holistic data repairs with probabilistic inference"
  Abstract: We introduce HoloClean, a framework for holistic data repairing driven by probabilistic inference. HoloClean unifies existing qualitative data repairing approaches, which rely on integrity constraints or external data sources, with quantitative data repairing methods, which leverage statistical properties of the input data. Given an inconsistent dataset as input, HoloClean automatically generates a probabilistic program that performs data repairing. Inspired by recent theoretical advances in probabilistic inference, we introduce a series of optimizations which ensure that inference over HoloClean's probabilistic model scales to instances with millions of tuples. We show that HoloClean scales to instances with millions of tuples and find data repairs with an average precision of ~90% and an average recall of above ~76% across a diverse array of datasets exhibiting different types of errors. This yields an average F1 improvement of more than 2x against state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "benchmark and survey of automated machine learning frameworks"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning in practice state of the art and recent results"
    - "alphaclean automatic generation of data cleaning pipelines"
    - "automl a survey of the state of the art"
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"

Connected through hyperedge HE-2 (induced by source paper "robust mean estimation under coordinate level corruption"):
  with members:
    - "faster algorithms for high dimensional robust covariance estimation"
    - "holodetect few shot learning for error detection"
    - "how hard is robust mean estimation"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "learning from untrusted data"

Connected through hyperedge HE-3 (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"
    - "snorkel rapid training data creation with weak supervision"
    - "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-4 (induced by source paper "survey on automated machine learning"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning state of the art and open challenges"
    - "sparse recovery for orthogonal polynomial transforms"
    - "automl a survey of the state of the art"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "autotune a derivative free optimization framework for hyperparameter tuning"

Connected through hyperedge HE-5 (induced by source paper "holodetect few shot learning for error detection"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "a formal framework for probabilistic unclean databases"
    - "on calibration of modern neural networks"
    - "snorkel rapid training data creation with weak supervision"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "wide deep learning for recommender systems"
    - "understanding deep learning requires rethinking generalization"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "systemds a declarative machine learning system for the end to end data science lifecycle"):
  with members:
    - "sato contextual semantic type detection in tables"
    - "holodetect few shot learning for error detection"
    - "sherlock a deep learning approach to semantic data type detection"
    - "towards federated learning at scale system design"
    - "autograph imperative style coding with graph based performance"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "autoaugment learning augmentation policies from data"
    - "helix holistic optimization for accelerating iterative machine learning"

Connected through hyperedge HE-7 (induced by source paper "crosstrainer practical domain adaptation with loss reweighting"):
  with members:
    - "a survey on deep transfer learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "snorkel rapid training data creation with weak supervision"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "return of frustratingly easy domain adaptation"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "towards interpretable and learnable risk analysis for entity resolution"):
  with members:
    - "r humo a risk aware human machine cooperation framework for entity resolution with quality guarantees"
    - "deep anomaly detection with outlier exposure"
    - "discriminative active learning"
    - "on calibration of modern neural networks"
    - "concrete problems in ai safety"
    - "why should i trust you explaining the predictions of any classifier"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "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.DB(Databases)