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: "lightrnn memory and computation efficient recurrent neural networks"
  Abstract: Recurrent neural networks (RNNs) have achieved state-of-the-art performances in many natural language processing tasks, such as language modeling and machine translation. However, when the vocabulary is large, the RNN model will become very big (e.g., possibly beyond the memory capacity of a GPU device) and its training will become very inefficient. In this work, we propose a novel technique to tackle this challenge. The key idea is to use 2-Component (2C) shared embedding for word representations. We allocate every word in the vocabulary into a table, each row of which is associated with a vector, and each column associated with another vector. Depending on its position in the table, a word is jointly represented by two components: a row vector and a column vector. Since the words in the same row share the row vector and the words in the same column share the column vector, we only need $2 \sqrt{|V|}$ vectors to represent a vocabulary of $|V|$ unique words, which are far less than the $|V|$ vectors required by existing approaches. Based on the 2-Component shared embedding, we design a new RNN algorithm and evaluate it using the language modeling task on several benchmark datasets. The results show that our algorithm significantly reduces the model size and speeds up the training process, without sacrifice of accuracy (it achieves similar, if not better, perplexity as compared to state-of-the-art language models). Remarkably, on the One-Billion-Word benchmark Dataset, our algorithm achieves comparable perplexity to previous language models, whilst reducing the model size by a factor of 40-100, and speeding up the training process by a factor of 2. We name our proposed algorithm \emph{LightRNN} to reflect its very small model size and very high training speed.

Connected through hyperedge HE-1 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "contrastive learning for image captioning"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "image captioning with sparse recurrent neural network"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "comic toward a compact image captioning model with attention"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "grow and prune compact fast and accurate lstms"
    - "compressing neural networks using the variational information bottleneck"

Connected through hyperedge HE-3 (induced by source paper "fast and simple mixture of softmaxes with bpe and hybrid lightrnn for language generation"):
  with members:
    - "universal transformers"
    - "neural machine translation with adequacy oriented learning"
    - "weighted transformer network for machine translation"
    - "from credit assignment to entropy regularization two new algorithms for neural sequence prediction"
    - "self attention with relative position representations"
    - "scaling neural machine translation"
    - "understanding back translation at scale"
    - "thumt an open source toolkit for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "slim embedding layers for recurrent neural language models"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "efficient softmax approximation for gpus"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "language modeling with gated convolutional networks"
    - "compressing neural language models by sparse word representations"
    - "recurrent highway networks"
    - "using the output embedding to improve language models"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-5 (induced by source paper "learning k way d dimensional discrete code for compact embedding representations"):
  with members:
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "entity embedding based anomaly detection for heterogeneous categorical events"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "bag of tricks for efficient text classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-6 (induced by source paper "an interpretable knowledge transfer model for knowledge base completion"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "neighborhood mixture model for knowledge base completion"
    - "link prediction using embedded knowledge graphs"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "transfer learning for low resource neural machine translation"
    - "sparse overcomplete word vector representations"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-7 (induced by source paper "character level language modeling with hierarchical recurrent neural networks"):
  with members:
    - "gated word character recurrent language model"
    - "exploring the limits of language modeling"
    - "character level incremental speech recognition with recurrent neural networks"
    - "single stream parallelization of generalized lstm like rnns on a gpu"
    - "character based neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "character aware neural language models"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"

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.CL(Computation and Language)