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: "datahub collaborative data science dataset version management at scale"
  Abstract: Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "how do data science workers collaborate roles workflows and tools"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "autoaiviz opening the blackbox of automated artificial intelligence with conditional parallel coordinates"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "an admm based framework for automl pipeline configuration"
    - "survey on automated machine learning"
    - "datasheets for datasets"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "data vision learning to see through algorithmic abstraction"

Connected through hyperedge HE-3 (induced by source paper "analysis of indexing structures for immutable data"):
  with members:
    - "blockbench a framework for analyzing private blockchains"
    - "untangling blockchain a data processing view of blockchain systems"
    - "a critique of ansi sql isolation levels"

Connected through hyperedge HE-4 (induced by source paper "automated machine learning state of the art and open challenges"):
  with members:
    - "learning internal representations"
    - "meta learning a survey"
    - "autostacker a compositional evolutionary learning system"
    - "regularized evolution for image classifier architecture search"
    - "rafiki machine learning as an analytics service system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-5 (induced by source paper "the machine learning bazaar harnessing the ml ecosystem for effective system development"):
  with members:
    - "neuronblocks building your nlp dnn models like playing lego"
    - "massively parallel hyperparameter tuning"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "non stochastic best arm identification and hyperparameter optimization"

Connected through hyperedge HE-6 (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-7 (induced by source paper "visual analytics for automated model discovery"):
  with members:
    - "clustrophile 2 guided visual clustering analysis"
    - "prediction factory automated development and collaborative evaluation of predictive models"
    - "auto keras an efficient neural architecture search system"
    - "do convolutional neural networks learn class hierarchy"
    - "rnnbow visualizing learning via backpropagation gradients in rnns"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards better analysis of deep convolutional neural networks"
    - "what may visualization processes optimize"

Connected through hyperedge HE-8 (induced by source paper "datasheets for datasets"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "baselines and a datasheet for the cerema awp dataset"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "quac question answering in context"
    - "semantics derived automatically from language corpora contain human like biases"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

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)