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: "dependency based convolutional neural networks for sentence embedding"
  Abstract: In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning and linguistic structures, we propose a tree-based convolutional neural network model which exploit various long-distance relationships between words. Our model improves the sequential baselines on all three sentiment and question classification tasks, and achieves the highest published accuracy on TREC.

Connected through hyperedge HE-1 (induced by source paper "sentence encoding with tree constrained relation networks"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "neural probabilistic model for non projective mst parsing"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"):
  with members:
    - "pubmed 200k rct a dataset for sequential sentence classification in medical abstracts"
    - "enriching word vectors with subword information"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "recurrent neural network for text classification with multi task learning"
    - "dropout with expectation linear regularization"
    - "bag of tricks for efficient text classification"
    - "neural architectures for named entity recognition"
    - "neural networks for joint sentence classification in medical paper abstracts"

Connected through hyperedge HE-3 (induced by source paper "translations as additional contexts for sentence classification"):
  with members:
    - "learned in translation contextualized word vectors"
    - "enriching word vectors with subword information"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "learning distributed representations of sentences from unlabelled data"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "self adaptive hierarchical sentence model"
    - "neural machine translation by jointly learning to align and translate"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-4 (induced by source paper "improving medical short text classification with semantic expansion using word cluster embedding"):
  with members:
    - "bag of tricks for efficient text classification"
    - "a c lstm neural network for text classification"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "jointly learning sentence embeddings and syntax with unsupervised tree lstms"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "structured attention networks"
    - "attention is all you need"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "learning to compose task specific tree structures"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "easy first dependency parsing with hierarchical tree lstms"

Connected through hyperedge HE-6 (induced by source paper "a structured self attentive sentence embedding"):
  with members:
    - "attentive pooling networks"
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "a fast unified model for parsing and sentence understanding"
    - "learning distributed representations of sentences from unlabelled data"
    - "neural tree indexers for text understanding"

Connected through hyperedge HE-7 (induced by source paper "neural models for sequence chunking"):
  with members:
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"

Connected through hyperedge HE-8 (induced by source paper "jointly trained sequential labeling and classification by sparse attention neural networks"):
  with members:
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "long short term memory networks for machine reading"
    - "learning natural language inference with lstm"
    - "neural machine translation by jointly learning to align and translate"
    - "a convolutional neural network for modelling sentences"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "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)