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: "a bayesian approach to discovering truth from conflicting sources for data integration"
  Abstract: In practical data integration systems, it is common for the data sources being integrated to provide conflicting information about the same entity. Consequently, a major challenge for data integration is to derive the most complete and accurate integrated records from diverse and sometimes conflicting sources. We term this challenge the truth finding problem. We observe that some sources are generally more reliable than others, and therefore a good model of source quality is the key to solving the truth finding problem. In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision. In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality. In so doing, ours is also the first approach designed to merge multi-valued attribute types. Our method is scalable, due to an efficient sampling-based inference algorithm that needs very few iterations in practice and enjoys linear time complexity, with an even faster incremental variant. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches to the truth finding problem.

Connected through hyperedge HE-1 (induced by source paper "open knowledge enrichment for long tail entities"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "truth inference at scale a bayesian model for adjudicating highly redundant crowd annotations"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-2 (induced by source paper "an unsupervised bayesian neural network for truth discovery"):
  with members:
    - "sequential multi class labeling in crowdsourcing"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "a survey on truth discovery"
    - "generalized denoising auto encoders as generative models"

Connected through hyperedge HE-3 (induced by source paper "crowdsourced truth discovery in the presence of hierarchies for knowledge fusion"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "truth finding on the deep web is the problem solved"
    - "from data fusion to knowledge fusion"
    - "a survey on truth discovery"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-4 (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-5 (induced by source paper "towards differentially private truth discovery for crowd sensing systems"):
  with members:
    - "privacy friendly mobility analytics using aggregate location data"
    - "privacy concerns vs user behavior in community question answering"
    - "a survey on truth discovery"
    - "what can we learn privately"

Connected through hyperedge HE-6 (induced by source paper "using social network information in bayesian truth discovery"):
  with members:
    - "sequential multi class labeling in crowdsourcing"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "a survey on truth discovery"

Connected through hyperedge HE-7 (induced by source paper "restricted boltzmann machines for robust and fast latent truth discovery"):
  with members:
    - "a survey on truth discovery"
    - "truth discovery algorithms an experimental evaluation"

Connected through hyperedge HE-8 (induced by source paper "combining restricted boltzmann machines with neural networks for latent truth discovery"):
  with members:
    - "restricted boltzmann machines for robust and fast latent truth discovery"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"

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)