human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "autoer automated entity resolution using generative modelling"):
  Abstract: Entity resolution (ER) refers to the problem of identifying records in one or more relations that refer to the same real-world entity. ER has been extensively studied by the database community with supervised machine learning approaches achieving the state-of-the-art results. However, supervised ML requires many labeled examples, both matches and unmatches, which are expensive to obtain. In this paper, we investigate an important problem: how can we design an unsupervised algorithm for ER that can achieve performance comparable to supervised approaches? We propose an automated ER solution, AutoER, that requires zero labeled examples. Our central insight is that the similarity vectors for matches should look different from that of unmatches. A number of innovations are needed to translate the intuition into an actual algorithm for ER. We advocate for the use of generative models to capture the two similarity vector distributions (the match distribution and the unmatch distribution). We propose an expectation maximization based algorithm to learn the model parameters. Our algorithm addresses many practical challenges including feature correlations, model overfitting, class imbalance, and transitivity between matches. On six datasets from four different domains, we show that the performance of AutoER is comparable and sometimes even better than supervised ML approaches.

Includes members:
  Member "reuse and adaptation for entity resolution through transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end entity resolution for big data a survey")
  Member "snorkel rapid training data creation with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient covariance estimation from temporal data")
  Member "crowder crowdsourcing entity resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crowdsourced collective entity resolution with relational match propagation")
  Member "finding connected components on map reduce in logarithmic rounds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "projected subgradient methods for learning sparse gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gaussian graphical model exploration and selection in high dimension low sample size setting")

Question: Please tell me which class the source paper 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)