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: "rnn recurrent library for torch"
  Abstract: The rnn package provides components for implementing a wide range of Recurrent Neural Networks. It is built withing the framework of the Torch distribution for use with the nn package. The components have evolved from 3 iterations, each adding to the flexibility and capability of the package. All component modules inherit either the AbstractRecurrent or AbstractSequencer classes. Strong unit testing, continued backwards compatibility and access to supporting material are the principles followed during its development. The package is compared against existing implementations of two published papers.

Connected through hyperedge HE-1 (induced by source paper "comparing neural and n gram based language models for word segmentation"):
  with members:
    - "a non projective greedy dependency parser with bidirectional lstms"
    - "neural word segmentation learning for chinese"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "recurrent neural network regularization"
    - "empirical methods for compound splitting"

Connected through hyperedge HE-2 (induced by source paper "learning visual knowledge memory networks for visual question answering"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "cascade r cnn delving into high quality object detection"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "structured attentions for visual question answering"
    - "weakly supervised dense video captioning"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "attentive pooling networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "opennmt open source toolkit for neural machine translation"):
  with members:
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "sequence to sequence rnns for text summarization"
    - "linguistic input features improve neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "what you get is what you see a visual markup decompiler"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-5 (induced by source paper "hadamard product for low rank bilinear pooling"):
  with members:
    - "visual question answering datasets algorithms and future challenges"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "a focused dynamic attention model for visual question answering"
    - "ask your neurons a deep learning approach to visual question answering"
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "a compositional object based approach to learning physical dynamics"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "understanding visual concepts with continuation learning"
    - "gated graph sequence neural networks"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning to compose neural networks for question answering"
    - "building machines that learn and think like people"
    - "learning physical intuition of block towers by example"

Connected through hyperedge HE-7 (induced by source paper "visual analysis of hidden state dynamics in recurrent neural networks"):
  with members:
    - "predicting process behaviour using deep learning"
    - "towards better analysis of deep convolutional neural networks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "visualizing and understanding neural models in nlp"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "visualizing and understanding recurrent networks"
    - "semi supervised sequence learning"

Connected through hyperedge HE-8 (induced by source paper "fine grained analysis of sentence embeddings using auxiliary prediction tasks"):
  with members:
    - "word ordering without syntax"
    - "representation of linguistic form and function in recurrent neural networks"
    - "learning distributed representations of sentences from unlabelled data"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "skip thought vectors"
    - "visualizing and understanding recurrent networks"
    - "semi supervised sequence learning"
    - "adam a method for stochastic optimization"

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)