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: "fast dpp sampling for nystr om with application to kernel methods"
  Abstract: The Nystr\"om method has long been popular for scaling up kernel methods. Its theoretical guarantees and empirical performance rely critically on the quality of the landmarks selected. We study landmark selection for Nystr\"om using Determinantal Point Processes (DPPs), discrete probability models that allow tractable generation of diverse samples. We prove that landmarks selected via DPPs guarantee bounds on approximation errors; subsequently, we analyze implications for kernel ridge regression. Contrary to prior reservations due to cubic complexity of DPPsampling, we show that (under certain conditions) Markov chain DPP sampling requires only linear time in the size of the data. We present several empirical results that support our theoretical analysis, and demonstrate the superior performance of DPP-based landmark selection compared with existing approaches.

Connected through hyperedge HE-1 (induced by source paper "submodular batch selection for training deep neural networks"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "self paced learning with adaptive deep visual embeddings"
    - "determinantal point processes for mini batch diversification"
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"
    - "biased importance sampling for deep neural network training"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "online batch selection for faster training of neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning nonsymmetric determinantal point processes"):
  with members:
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "kronecker determinantal point processes"
    - "fixed point algorithms for learning determinantal point processes"
    - "adam a method for stochastic optimization"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-3 (induced by source paper "learning determinantal point processes by corrective negative sampling"):
  with members:
    - "adversarial contrastive estimation"
    - "improving negative sampling for word representation using self embedded features"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "training region based object detectors with online hard example mining"
    - "kronecker determinantal point processes"
    - "fixed point algorithms for learning determinantal point processes"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-4 (induced by source paper "vbald variational bayesian approximation of log determinants"):
  with members:
    - "entropic trace estimates for log determinants"
    - "faster greedy map inference for determinantal point processes"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "improved bounds on sample size for implicit matrix trace estimators"

Connected through hyperedge HE-5 (induced by source paper "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"):
  with members:
    - "faster coordinate descent via adaptive importance sampling"
    - "importance sampling for minibatches"
    - "learning visual features from large weakly supervised data"
    - "efficient sampling for k determinantal point processes"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "recursive sampling for the nystr om method"):
  with members:
    - "relative error embeddings for the gaussian kernel distance"
    - "on column selection in approximate kernel canonical correlation analysis"
    - "large scale kernel learning using block coordinate descent"
    - "ridge leverage scores for low rank approximation"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "streaming kernel principal component analysis"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-7 (induced by source paper "batched gaussian process bandit optimization via determinantal point processes"):
  with members:
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "randomized rounding for the largest simplex problem"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "exponential inapproximability of selecting a maximum volume sub matrix"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-8 (induced by source paper "kronecker determinantal point processes"):
  with members:
    - "fixed point algorithms for learning determinantal point processes"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "efficient sampling for k determinantal point processes"
    - "diversifying sparsity using variational determinantal point processes"
    - "learning mixtures of submodular shells with application to document summarization"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

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)