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: "holodetect few shot learning for error detection"
  Abstract: We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement. Our framework consists of two parts: (1) an expressive model to learn rich representations that capture the inherent syntactic and semantic heterogeneity of errors; and (2) a data augmentation model that, given a small seed of clean records, uses dataset-specific transformations to automatically generate additional training data. Our key insight is to learn data augmentation policies from the noisy input dataset in a weakly supervised manner. We show that our framework detects errors with an average precision of ~94% and an average recall of ~93% across a diverse array of datasets that exhibit different types and amounts of errors. We compare our approach to a comprehensive collection of error detection methods, ranging from traditional rule-based methods to ensemble-based and active learning approaches. We show that data augmentation yields an average improvement of 20 F1 points while it requires access to 3x fewer labeled examples compared to other ML approaches.

Connected through hyperedge HE-1 (induced by source paper "robust mean estimation under coordinate level corruption"):
  with members:
    - "faster algorithms for high dimensional robust covariance estimation"
    - "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"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning from untrusted data"

Connected through hyperedge HE-2 (induced by source paper "priu a provenance based approach for incrementally updating regression models"):
  with members:
    - "manipulating and measuring model interpretability"
    - "boostclean automated error detection and repair for machine learning"
    - "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-3 (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"
    - "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"
    - "incremental view maintenance with triple lock factorization benefits"

Connected through hyperedge HE-4 (induced by source paper "selectivity estimation with deep likelihood models"):
  with members:
    - "neo a learned query optimizer"
    - "multi attribute selectivity estimation using deep learning"
    - "how i learned to stop worrying and love re optimization"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "quicksel quick selectivity learning with mixture models"

Connected through hyperedge HE-5 (induced by source paper "approximate inference in structured instances with noisy categorical observations"):
  with members:
    - "a formal framework for probabilistic unclean databases"
    - "inference in sparse graphs with pairwise measurements and side information"
    - "holoclean holistic data repairs with probabilistic inference"
    - "harnessing deep neural networks with logic rules"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "bidirectional lstm crf models for sequence tagging"
    - "incremental knowledge base construction using deepdive"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-6 (induced by source paper "toward a view based data cleaning architecture"):
  with members:
    - "a formal framework for probabilistic unclean databases"
    - "minimal change integrity maintenance using tuple deletions"

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)