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: "deep sentence embedding using the long short term memory networks"
  Abstract: This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detects the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods.

Connected through hyperedge HE-1 (induced by source paper "self taught convolutional neural networks for short text clustering"):
  with members:
    - "end to end memory networks"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "unsupervised learning of visual representations using videos"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "neural responding machine for short text conversation"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "lstm based predictions for proactive information retrieval"):
  with members:
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "distributed representations of sentences and documents"
    - "recurrent neural network regularization"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "generating sequences with recurrent neural networks"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-3 (induced by source paper "learning to distill the essence vector modeling framework"):
  with members:
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "representation learning a review and new perspectives"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-4 (induced by source paper "relation classification via recurrent neural network"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "distributed representations of words and phrases and their compositionality"
    - "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)