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: "natural language processing with small feed forward networks"
  Abstract: We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.

Connected through hyperedge HE-1 (induced by source paper "federated learning for emoji prediction in a mobile keyboard"):
  with members:
    - "towards federated learning at scale system design"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning for mobile keyboard prediction"
    - "a fast compact accurate model for language identification of codemixed text"
    - "universal language model fine tuning for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "lstm a search space odyssey"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-2 (induced by source paper "smaller text classifiers with discriminative cluster embeddings"):
  with members:
    - "hash embeddings for efficient word representations"
    - "fasttext zip compressing text classification models"
    - "compressing word embeddings via deep compositional code learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "character level convolutional networks for text classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "cosine compressive network embedding on large scale information networks"):
  with members:
    - "mile a multi level framework for scalable graph embedding"
    - "word2bits quantized word vectors"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "compressing word embeddings via deep compositional code learning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-4 (induced by source paper "progress and tradeoffs in neural language models"):
  with members:
    - "fast parametric learning with activation memorization"
    - "an analysis of neural language modeling at multiple scales"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "quasi recurrent neural networks"
    - "improving neural language models with a continuous cache"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"):
  with members:
    - "a joint model for word embedding and word morphology"
    - "globally normalized transition based neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "a fast compact accurate model for language identification of codemixed text"):
  with members:
    - "universal dependency parsing for hindi english code switching"
    - "fasttext zip compressing text classification models"
    - "structured training for neural network transition based parsing"

Connected through hyperedge HE-7 (induced by source paper "recursive neural network based preordering for english to japanese machine translation"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "just ask building an architecture for extensible self service spoken language understanding"):
  with members:
    - "progressive neural networks"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a deep reinforcement learning chatbot"
    - "transfer learning for neural semantic parsing"
    - "data recombination for neural semantic parsing"
    - "neural architectures for named entity recognition"
    - "attention based recurrent neural network models for joint intent detection and slot filling"

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)