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: "blocks and fuel frameworks for deep learning"
  Abstract: We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support (Bastien et al., 2012; Bergstra et al., 2010). It facilitates the training of complex neural network models by providing parametrized Theano operations, attaching metadata to Theano’s symbolic computational graph, and providing an extensive set of utilities to assist training the networks, e.g. training algorithms, logging, monitoring, visualization, and serialization. Fuel provides a standard format for machine learning datasets. It allows the user to easily iterate over large datasets, performing many types of pre-processing on the fly.

Connected through hyperedge HE-1 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "bigdl a distributed deep learning framework for big data"
    - "improving data quality through deep learning and statistical models"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "benchmarking deep learning hardware and frameworks qualitative metrics"):
  with members:
    - "bigdl a distributed deep learning framework for big data"
    - "improving data quality through deep learning and statistical models"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "purine a bi graph based deep learning framework"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "why not be versatile applications of the sgnmt decoder for machine translation"):
  with members:
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "unfolding and shrinking neural machine translation ensembles"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural machine translation by minimising the bayes risk with respect to syntactic translation lattices"
    - "syntactically guided neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "learning to compute word embeddings on the fly"):
  with members:
    - "dynamic integration of background knowledge in neural nlu systems"
    - "vocabulary selection strategies for neural machine translation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a comparative study of word embeddings for reading comprehension"
    - "leveraging lexical resources for learning entity embeddings in multi relational data"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "squad 100 000 questions for machine comprehension of text"
    - "vocabulary manipulation for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "towards two dimensional sequence to sequence model in neural machine translation"):
  with members:
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "sena cnn overcoming catastrophic forgetting in convolutional neural networks by selective network augmentation"):
  with members:
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "understanding trained cnns by indexing neuron selectivity"
    - "less forgetting learning in deep neural networks"
    - "learning without forgetting"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-7 (induced by source paper "a tutorial on deep learning for music information retrieval"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "a neural parametric singing synthesizer"
    - "revisiting the problem of audio based hit song prediction using convolutional neural networks"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-8 (induced by source paper "on multilingual training of neural dependency parsers"):
  with members:
    - "syntaxnet models for the conll 2017 shared task"
    - "read tag and parse all at once or fully neural dependency parsing"
    - "deep biaffine attention for neural dependency parsing"
    - "globally normalized transition based neural networks"
    - "dependency parsing as head selection"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "exploring the limits of language modeling"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)