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: "benchmarking of lstm networks"
  Abstract: LSTM (Long Short-Term Memory) recurrent neural networks have been highly successful in a number of application areas. This technical report describes the use of the MNIST and UW3 databases for benchmarking LSTM networks and explores the effect of different architectural and hyperparameter choices on performance. Significant findings include: (1) LSTM performance depends smoothly on learning rates, (2) batching and momentum has no significant effect on performance, (3) softmax training outperforms least square training, (4) peephole units are not useful, (5) the standard non-linearities (tanh and sigmoid) perform best, (6) bidirectional training combined with CTC performs better than other methods.

Connected through hyperedge HE-1 (induced by source paper "measuring the effects of data parallelism on neural network training"):
  with members:
    - "introduction to online convex optimization"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "large scale distributed neural network training through online distillation"
    - "don t use large mini batches use local sgd"
    - "revisiting small batch training for deep neural networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "imagenet training in minutes"

Connected through hyperedge HE-2 (induced by source paper "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"):
  with members:
    - "alternating multi bit quantization for recurrent neural networks"
    - "lstm a search space odyssey"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "the implementation of a deep recurrent neural network language model on a xilinx fpga"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"):
  with members:
    - "lstm a search space odyssey"
    - "associative long short term memory"
    - "returnn the rwth extensible training framework for universal recurrent neural networks"
    - "deep learning in neural networks"
    - "deep recurrent neural networks for acoustic modelling"
    - "deep residual learning for image recognition"
    - "highway long short term memory rnns for distant speech recognition"
    - "training very deep networks"

Connected through hyperedge HE-4 (induced by source paper "implicit language model in lstm for ocr"):
  with members:
    - "lstm a search space odyssey"
    - "visualizing and understanding recurrent networks"
    - "how to construct deep recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning with encoder decoder recurrent neural networks experiments with motion capture sequences"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "stacked generative adversarial networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "generative adversarial text to image synthesis"
    - "contextual rnn gans for abstract reasoning diagram generation"
    - "theano a python framework for fast computation of mathematical expressions"

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.NE(Neural and Evolutionary Computing)