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: "dependency recurrent neural language models for sentence completion"
  Abstract: Recent work on language modelling has shifted focus from count-based models to neural models. In these works, the words in each sentence are always considered in a left-to-right order. In this paper we show how we can improve the performance of the recurrent neural network (RNN) language model by incorporating the syntactic dependencies of a sentence, which have the effect of bringing relevant contexts closer to the word being predicted. We evaluate our approach on the Microsoft Research Sentence Completion Challenge and show that the dependency RNN proposed improves over the RNN by about 10 points in accuracy. Furthermore, we achieve results comparable with the state-of-the-art models on this task.

Connected through hyperedge HE-1 (induced by source paper "scalable syntax aware language models using knowledge distillation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "assessing bert s syntactic abilities"
    - "universal transformers"
    - "finding syntax in human encephalography with beam search"
    - "distilling knowledge for search based structured prediction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "pod positional dependency based word embedding for aspect term extraction"):
  with members:
    - "language models with transformers"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "aspect term extraction with history attention and selective transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "elixa a modular and flexible absa platform"

Connected through hyperedge HE-3 (induced by source paper "discovering signals from web sources to predict cyber attacks"):
  with members:
    - "early warnings of cyber threats in online discussions"
    - "rnn based early cyber attack detection for the tennessee eastman process"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "adam a method for stochastic optimization"
    - "a clockwork rnn"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "syntax aware language modeling with recurrent neural networks"):
  with members:
    - "exploring the limits of language modeling"
    - "character aware neural language models"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "improving negative sampling for word representation using self embedded features"):
  with members:
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "neural programmer inducing latent programs with gradient descent"
    - "wordrank learning word embeddings via robust ranking"
    - "character aware neural language models"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "recurrent memory networks for language modeling"):
  with members:
    - "lstm a search space odyssey"
    - "end to end memory networks"
    - "transition based dependency parsing with stack long short term memory"
    - "grid long short term memory"
    - "strategies for training large vocabulary neural language models"
    - "visualizing and understanding recurrent networks"
    - "tree structured composition in neural networks without tree structured architectures"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-7 (induced by source paper "generative knowledge transfer for neural language models"):
  with members:
    - "learning privately from multiparty data"
    - "numerically grounded language models for semantic error correction"
    - "wavenet a generative model for raw audio"
    - "character level incremental speech recognition with recurrent neural networks"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "transferring knowledge from a rnn to a dnn"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-8 (induced by source paper "recurrent memory network for language modeling"):
  with members:
    - "lstm a search space odyssey"
    - "transition based dependency parsing with stack long short term memory"
    - "grid long short term memory"
    - "visualizing and understanding recurrent networks"
    - "draw a recurrent neural network for image generation"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural turing machines"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)