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: "stochastic learning on imbalanced data 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 Balanced Mini-batch SGD (BM-SGD). We show that regular SGD and stratified sampling emerge as special cases. Furthermore, BM-SGD can be considered a generalization of 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 "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"
    - "determinantal point processes for mini batch diversification"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "batch active preference based learning of reward functions"):
  with members:
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "continuous inverse optimal control with locally optimal examples"
    - "april active preference learning based reinforcement learning"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-4 (induced by source paper "scalable recollections for continual lifelong learning"):
  with members:
    - "automated curriculum learning for neural networks"
    - "knowledge distillation using unlabeled mismatched images"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "generative adversarial active learning"
    - "continual learning with deep generative replay"
    - "neural episodic control"
    - "learning to remember rare events"

Connected through hyperedge HE-5 (induced by source paper "stochastic optimization with bandit sampling"):
  with members:
    - "importance sampling for minibatches"
    - "improved svrg for non strongly convex or sum of non convex objectives"

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)