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: "accelerating recurrent neural network training using sequence bucketing and multi gpu data parallelization"
  Abstract: An efficient algorithm for recurrent neural network training is presented. The approach increases the training speed for tasks where a length of the input sequence may vary significantly. The proposed approach is based on the optimal batch bucketing by input sequence length and data parallelization on multiple graphical processing units. The baseline training performance without sequence bucketing is compared with the proposed solution for a different number of buckets. An example is given for the online handwriting recognition task using an LSTM recurrent neural network. The evaluation is performed in terms of the wall clock time, number of epochs, and validation loss value.

Connected through hyperedge HE-1 (induced by source paper "etalumis bringing probabilistic programming to scientific simulators at scale"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "pyro deep universal probabilistic programming"
    - "an empirical model of large batch training"
    - "measuring the effects of data parallelism on neural network training"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "scaling sgd batch size to 32k for imagenet training"
    - "carla an open urban driving simulator"
    - "improvements to inference compilation for probabilistic programming in large scale scientific simulators"

Connected through hyperedge HE-2 (induced by source paper "learning to update for object tracking with recurrent meta learner"):
  with members:
    - "good features to correlate for visual tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "hierarchical attentive recurrent tracking"
    - "learning spatial aware regressions for visual tracking"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "learning policies for adaptive tracking with deep feature cascades"

Connected through hyperedge HE-3 (induced by source paper "an optimized and energy efficient parallel implementation of non iteratively trained recurrent neural networks"):
  with members:
    - "scaling back propagation by parallel scan algorithm"
    - "memory visualization for gated recurrent neural networks in speech recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep learning in neural networks"
    - "attention based models for speech recognition"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "manifold mixup improves text recognition with ctc loss"):
  with members:
    - "adversarial generation of handwritten text images conditioned on sequences"
    - "mixup as locally linear out of manifold regularization"
    - "mixup beyond empirical risk minimization"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "generating sequences with recurrent neural networks"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "an empirical exploration of curriculum learning for neural machine translation"):
  with members:
    - "scheduled multi task learning from syntax to translation"
    - "automated curriculum learning for neural networks"
    - "sockeye a toolkit for neural machine translation"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "dynamic data selection for neural machine translation"
    - "six challenges for neural machine translation"
    - "boosting neural machine translation"
    - "visualizing and understanding curriculum learning for long short term memory networks"

Connected through hyperedge HE-6 (induced by source paper "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"):
  with members:
    - "returnn the rwth extensible training framework for universal recurrent neural networks"
    - "achieving human parity in conversational speech recognition"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed 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)