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: "lstm benchmarks for deep learning frameworks"
  Abstract: This study provides benchmarks for different implementations of LSTM units between the deep learning frameworks PyTorch, TensorFlow, Lasagne and Keras. The comparison includes cuDNN LSTMs, fused LSTM variants and less optimized, but more flexible LSTM implementations. The benchmarks reflect two typical scenarios for automatic speech recognition, notably continuous speech recognition and isolated digit recognition. These scenarios cover input sequences of fixed and variable length as well as the loss functions CTC and cross entropy. Additionally, a comparison between four different PyTorch versions is included. The code is available online this https URL.

Connected through hyperedge HE-1 (induced by source paper "tapirxla embedding fork join parallelism into the xla compiler in tensorflow using tapir"):
  with members:
    - "don t unroll adjoint differentiating ssa form programs"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "glow graph lowering compiler techniques for neural networks"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "imagenet training in minutes"
    - "neural collaborative filtering"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-2 (induced by source paper "modelling sequential music track skips using a multi rnn approach"):
  with members:
    - "the music streaming sessions dataset"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow a system for large scale machine learning"
    - "session based recommendations with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "across stack profiling and characterization of machine learning models on gpus"):
  with members:
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "performance modelling of deep learning on intel many integrated core architectures"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "frustrated with replicating claims of a shared model a solution"
    - "characterizing co located datacenter workloads an alibaba case study"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "frustrated with replicating claims of a shared model a solution"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "latency and throughput characterization of convolutional neural networks for mobile computer vision"
    - "characterizing co located datacenter workloads an alibaba case study"
    - "tbd benchmarking and analyzing deep neural network training"
    - "tensorflow serving flexible high performance ml serving"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dlpaper2code auto generation of code from deep learning research papers"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-5 (induced by source paper "machine learning for semi linear pdes"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "practical recommendations for gradient based training of deep architectures"

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)