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: "modelling sentence pairs with tree structured attentive encoder"
  Abstract: We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs. Since existing attentive models exert attention on the sequential structure, we propose a way to incorporate attention into the tree topology. Specially, given a pair of sentences, our attentive encoder uses the representation of one sentence, which generated via an RNN, to guide the structural encoding of the other sentence on the dependency parse tree. We evaluate the proposed attentive encoder on three tasks: semantic similarity, paraphrase identification and true-false question selection. Experimental results show that our encoder outperforms all baselines and achieves state-of-the-art results on two tasks.

Connected through hyperedge HE-1 (induced by source paper "improving tree lstm with tree attention"):
  with members:
    - "sentence state lstm for text representation"
    - "evaluating compositionality in sentence embeddings"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-2 (induced by source paper "a spark ml driven preprocessing approach for deep learning based scholarly data applications"):
  with members:
    - "scake semantic connectivity aware keyword extraction"
    - "pabed a tool for big education data analysis"
    - "deep keyphrase generation"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "recurrent neural network for text classification with multi task learning"
    - "sequence to sequence rnns for text summarization"
    - "mllib machine learning in apache spark"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "regmapr text matching made easy"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "enhancing sentence embedding with generalized pooling"
    - "natural language inference over interaction space"
    - "on the state of the art of evaluation in neural language models"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "shortcut stacked sentence encoders for multi domain inference"

Connected through hyperedge HE-4 (induced by source paper "towards bidirectional hierarchical representations for attention based neural machine translation"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "syntactically guided neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "a joint many task model growing a neural network for multiple nlp tasks"):
  with members:
    - "progressive neural networks"
    - "neural machine translation with source side latent graph parsing"
    - "tree to sequence attentional neural machine translation"
    - "cross stitch networks for multi task learning"
    - "gated word character recurrent language model"
    - "enriching word vectors with subword information"
    - "globally normalized transition based neural networks"
    - "learning without forgetting"

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)