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: "determinantal point processes for mini batch diversification"
  Abstract: We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a non-uniform sampling scheme based on the Determinantal Point Process (DPP). The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data. This simultaneously balances the data and leads to stochastic gradients with lower variance. We term this approach Diversified Mini-Batch SGD (DM-SGD). We show that regular SGD and a biased version of stratified sampling emerge as special cases. Furthermore, DM-SGD generalizes stratified sampling to cases where no discrete features exist to bin the data into groups. We show experimentally that our method results more interpretable and diverse features in unsupervised setups, and in better classification accuracies in supervised setups.

Connected through hyperedge HE-1 (induced by source paper "fine tuning pretrained language models weight initializations data orders and early stopping"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "show your work improved reporting of experimental results"
    - "albert a lite bert for self supervised learning of language representations"
    - "freelb enhanced adversarial training for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"

Connected through hyperedge HE-3 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds"):
  with members:
    - "on the difficulty of warm starting neural network training"
    - "adversarial active learning for deep networks a margin based approach"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "libact pool based active learning in python"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"

Connected through hyperedge HE-4 (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"
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"
    - "biased importance sampling for deep neural network training"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "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-5 (induced by source paper "advances in variational inference"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "iterative amortized inference"
    - "inference suboptimality in variational autoencoders"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "faster coordinate descent via adaptive importance sampling"
    - "the numerics of gans"
    - "stein variational policy gradient"

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)