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: "slimfast guaranteed results for data fusion and source reliability"
  Abstract: We focus on data fusion, i.e., the problem of unifying conflicting data from data sources into a single representation by estimating the source accuracies. We propose SLiMFast, a framework that expresses data fusion as a statistical learning problem over discriminative probabilistic models, which in many cases correspond to logistic regression. In contrast to previous approaches that use complex generative models, discriminative models make fewer distributional assumptions over data sources and allow us to obtain rigorous theoretical guarantees. Furthermore, we show how SLiMFast enables incorporating domain knowledge into data fusion, yielding accuracy improvements of up to 50\% over state-of-the-art baselines. Building upon our theoretical results, we design an optimizer that obviates the need for users to manually select an algorithm for learning SLiMFast's parameters. We validate our optimizer on multiple real-world datasets and show that it can accurately predict the learning algorithm that yields the best data fusion results.

Connected through hyperedge HE-1 (induced by source paper "goggles automatic training data generation with affinity coding"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "fusing data with correlations"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "technical report optimizing human involvement for entity matching and consolidation"):
  with members:
    - "entity consolidation the golden record problem"
    - "synthesizing mapping relationships using table corpus"
    - "leveraging transitive relations for crowdsourced joins"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-3 (induced by source paper "snorkel drybell a case study in deploying weak supervision at industrial scale"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "exploring the limits of weakly supervised pretraining"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "visual discovery at pinterest"

Connected through hyperedge HE-4 (induced by source paper "snorkel rapid training data creation with weak supervision"):
  with members:
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "holoclean holistic data repairs with probabilistic inference"):
  with members:
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "truth finding on the deep web is the problem solved"
    - "incremental knowledge base construction using deepdive"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "dimmwitted a study of main memory statistical analytics"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-6 (induced by source paper "infrastructure for usable machine learning the stanford dawn project"):
  with members:
    - "asap prioritizing attention via time series smoothing"
    - "learning the structure of generative models without labeled data"
    - "noscope optimizing neural network queries over video at scale"
    - "macrobase prioritizing attention in fast data"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "socratic learning augmenting generative models to incorporate latent subsets in training data"
    - "a two pronged progress in structured dense matrix multiplication"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-7 (induced by source paper "unsupervised string transformation learning for entity consolidation"):
  with members:
    - "neural guided deductive search for real time program synthesis from examples"
    - "incremental knowledge base construction using deepdive"
    - "truth discovery algorithms an experimental evaluation"
    - "learning semantic string transformations from examples"
    - "pass join a partition based method for similarity joins"

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)