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: "learning semantic representations for the phrase translation model"
  Abstract: This paper presents a novel semantic-based phrase translation model. A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent semantic space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a multi-layer neural network whose weights are learned on parallel training data. The learning is aimed to directly optimize the quality of end-to-end machine translation results. Experimental evaluation has been performed on two Europarl translation tasks, English-French and German-English. The results show that the new semantic-based phrase translation model significantly improves the performance of a state-of-the-art phrase-based statistical machine translation sys-tem, leading to a gain of 0.7-1.0 BLEU points.

Connected through hyperedge HE-1 (induced by source paper "semantic classification of tabular datasets via character level convolutional neural networks"):
  with members:
    - "how transferable are neural networks in nlp applications"
    - "character level convolutional networks for text classification"
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "text understanding from scratch"):
  with members:
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "learning to execute"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "show and tell a neural image caption generator"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "learning phrase representations using rnn encoder decoder for statistical machine translation"):
  with members:
    - "an autoencoder approach to learning bilingual word representations"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "how to construct deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "barnes hut sne"
    - "adadelta an adaptive learning rate method"
    - "advances in optimizing recurrent networks"
    - "theano new features and speed improvements"

Connected through hyperedge HE-4 (induced by source paper "an autoencoder approach to learning bilingual word representations"):
  with members:
    - "multilingual models for compositional distributed semantics"
    - "exploiting similarities among languages for machine translation"
    - "multilingual distributed representations without word alignment"
    - "natural language processing almost from scratch"
    - "cross lingual annotation projection of semantic roles"

Connected through hyperedge HE-5 (induced by source paper "learning multilingual word representations using a bag of words autoencoder"):
  with members:
    - "exploiting similarities among languages for machine translation"
    - "natural language processing almost from scratch"

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)