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 "submodular batch selection for training deep neural networks"):
  Abstract: Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today. We introduce a mini-batch selection strategy based on submodular function maximization. Our novel submodular formulation captures the informativeness of each sample and diversity of the whole subset. We design an efficient, greedy algorithm which can give high-quality solutions to this NP-hard combinatorial optimization problem. Our extensive experiments on standard datasets show that the deep models trained using the proposed batch selection strategy provide better generalization than Stochastic Gradient Descent as well as a popular baseline sampling strategy across different learning rates, batch sizes, and distance metrics.

Includes members:
  Member "not all samples are created equal deep learning with importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no regret sample selection with noisy labels")
  Member "self paced learning with adaptive deep visual embeddings":
    also connected through other hyperedges:
      (none)
  Member "determinantal point processes for mini batch diversification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine tuning pretrained language models weight initializations data orders and early stopping")
  Member "stochastic learning on imbalanced data determinantal point processes for mini batch diversification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds")
  Member "biased importance sampling for deep neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets")
  Member "fast dpp sampling for nystr om with application to kernel methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning nonsymmetric determinantal point processes")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "online batch selection for faster training of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "derivative manipulation for general example weighting")

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.LG(Machine Learning)