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: "boostclean automated error detection and repair for machine learning"
  Abstract: Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to different types of inconsistencies, and new data streams during prediction time, the model may encounter previously unseen inconsistencies. An important class of such inconsistencies is domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy. We present BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting. BoostClean selects this ensemble from an extensible library that is pre-populated general detection functions, including a novel detector based on the Word2Vec deep learning model, which detects errors across a diverse set of domains. Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, real-world data analyses, and production datasets that show that Boost- Clean can increase absolute prediction accuracy by up to 9% over the best non-ensembled alternatives. Our optimizations including parallelism, materialization, and indexing techniques show a 22.2x end-to-end speedup on a 16-core machine.

Connected through hyperedge HE-1 (induced by source paper "priu a provenance based approach for incrementally updating regression models"):
  with members:
    - "holodetect few shot learning for error detection"
    - "manipulating and measuring model interpretability"
    - "the mythos of model interpretability"
    - "processing analytical workloads incrementally"
    - "linview incremental view maintenance for complex analytical queries"
    - "putting lipstick on pig enabling database style workflow provenance"
    - "provenance for aggregate queries"

Connected through hyperedge HE-2 (induced by source paper "cleanml a benchmark for joint data cleaning and machine learning experiments and analysis"):
  with members:
    - "don t use large mini batches use local sgd"
    - "impacts of dirty data and experimental evaluation"
    - "holoclean holistic data repairs with probabilistic inference"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "xgboost a scalable tree boosting system"
    - "controlling false discoveries during interactive data exploration"
    - "staleness aware async sgd for distributed deep learning"

Connected through hyperedge HE-3 (induced by source paper "ed2 two stage active learning for error detection technical report"):
  with members:
    - "holoclean holistic data repairs with probabilistic inference"
    - "xgboost a scalable tree boosting system"
    - "truth finding on the deep web is the problem solved"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on testing machine learning programs"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "deepevolution a search based testing approach for deep neural networks"
    - "tfcheck a tensorflow library for detecting training issues in neural network programs"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "impacts of dirty data and experimental evaluation"

Connected through hyperedge HE-6 (induced by source paper "similarity encoding for learning with dirty categorical variables"):
  with members:
    - "entity embeddings of categorical variables"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"
    - "feature hashing for large scale multitask 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)