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: "context dependent translation selection using convolutional neural network"
  Abstract: We propose a novel method for translation selection in statistical machine translation, in which a convolutional neural network is employed to judge the similarity between a phrase pair in two languages. The specifically designed convolutional architecture encodes not only the semantic similarity of the translation pair, but also the context containing the phrase in the source language. Therefore, our approach is able to capture context-dependent semantic similarities of translation pairs. We adopt a curriculum learning strategy to train the model: we classify the training examples into easy, medium, and difficult categories, and gradually build the ability of representing phrase and sentence level context by using training examples from easy to difficult. Experimental results show that our approach significantly outperforms the baseline system by up to 1.4 BLEU points.

Connected through hyperedge HE-1 (induced by source paper "recognising agreement and disagreement between stances with reason comparing networks"):
  with members:
    - "attention is all you need"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "bilateral multi perspective matching for natural language sentences"
    - "improving agreement and disagreement identification in online discussions with a socially tuned sentiment lexicon"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "stance detection with bidirectional conditional encoding"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-2 (induced by source paper "brain intelligence go beyond artificial intelligence"):
  with members:
    - "deep learning on fpgas past present and future"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "classifying relations by ranking with convolutional neural networks"
    - "deep residual learning for image recognition"
    - "multi task sequence to sequence learning"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-3 (induced by source paper "a convolutional encoder model for neural machine translation"):
  with members:
    - "vocabulary selection strategies for neural machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "vocabulary manipulation for neural machine translation"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "neural machine translation with recurrent attention modeling"
    - "edinburgh neural machine translation systems for wmt 16"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "memory enhanced decoder for neural machine translation"):
  with members:
    - "long short term memory networks for machine reading"
    - "modeling coverage for neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "neural transformation machine a new architecture for sequence to sequence learning"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "how to construct deep recurrent neural networks"

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)