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: "unsupervised learning of noisy or bayesian networks"
  Abstract: This paper considers the problem of learning the parameters in Bayesian networks of discrete variables with known structure and hidden variables. Previous approaches in these settings typically use expectation maximization; when the network has high treewidth, the required expectations might be approximated using Monte Carlo or variational methods. We show how to avoid inference altogether during learning by giving a polynomial-time algorithm based on the method-of-moments, building upon recent work on learning discrete-valued mixture models. In particular, we show how to learn the parameters for a family of bipartite noisy-or Bayesian networks. In our experimental results, we demonstrate an application of our algorithm to learning QMR-DT, a large Bayesian network used for medical diagnosis. We show that it is possible to fully learn the parameters of QMR-DT even when only the findings are observed in the training data (ground truth diseases unknown).

Connected through hyperedge HE-1 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "multiple instance learning convolutional neural networks for object recognition"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-2 (induced by source paper "amortized inference of variational bounds for learning noisy or"):
  with members:
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "recognition networks for approximate inference in bn20 networks"
    - "variational probabilistic inference and the qmr dt network"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-3 (induced by source paper "sequential local learning for latent graphical models"):
  with members:
    - "nonparametric estimation of multi view latent variable models"
    - "spectral experts for estimating mixtures of linear regressions"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent junction trees"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "deep learning with permutation invariant operator for multi instance histopathology classification"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "classifying and segmenting microscopy images with deep multiple instance learning"

Connected through hyperedge HE-5 (induced by source paper "provable learning of noisy or networks"):
  with members:
    - "polynomial time tensor decompositions with sum of squares"
    - "simple efficient and neural algorithms for sparse coding"
    - "learning mixtures of gaussians in high dimensions"
    - "an empirical analysis of likelihood weighting simulation on a large multiply connected belief network"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-6 (induced by source paper "variational hybridization and transformation for large inaccurate noisy or networks"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "addressing the rare word problem in neural machine translation"
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "loopy belief propagation for approximate inference an empirical study"
    - "formula based probabilistic inference"
    - "structured priors for structure learning"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-7 (induced by source paper "tensor factorization via matrix factorization"):
  with members:
    - "simultaneous diagonalization the asymmetric low rank and noisy settings"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "fast detection of overlapping communities via online tensor methods"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"
    - "symmetric tensor decomposition"
    - "most tensor problems are np hard"

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.LG(Machine Learning)