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: "a deep neural network approach to parallel sentence extraction"
  Abstract: Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose an end-to-end deep neural network approach to detect translational equivalence between sentences in two different languages. In contrast to previous approaches, which typically rely on multiples models and various word alignment features, by leveraging continuous vector representation of sentences we remove the need of any domain specific feature engineering. Using a siamese bidirectional recurrent neural networks, our results against a strong baseline based on a state-of-the-art parallel sentence extraction system show a significant improvement in both the quality of the extracted parallel sentences and the translation performance of statistical machine translation systems. We believe this study is the first one to investigate deep learning for the parallel sentence extraction task.

Connected through hyperedge HE-1 (induced by source paper "hierarchical document encoder for parallel corpus mining"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "word mover s embedding from word2vec to document embedding"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning cross lingual sentence representations via a multi task dual encoder model"
    - "efficient vector representation for documents through corruption"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning cross lingual sentence representations via a multi task dual encoder model"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "effective parallel corpus mining using bilingual sentence embeddings"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "achieving human parity on automatic chinese to english news translation"
    - "filtering and mining parallel data in a joint multilingual space"
    - "universal sentence encoder"
    - "attention is all you need"
    - "billion scale similarity search with gpus"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation for low resource languages using bilingual lexicon induced from comparable corpora"):
  with members:
    - "attention is all you need"
    - "the iit bombay english hindi parallel corpus"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)