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 sensitive convolutional neural networks for modeling sentences and documents"
  Abstract: The goal of sentence and document modeling is to accurately represent the meaning of sentences and documents for various Natural Language Processing tasks. In this work, we present Dependency Sensitive Convolutional Neural Networks (DSCNN) as a general-purpose classification system for both sentences and documents. DSCNN hierarchically builds textual representations by processing pretrained word embeddings via Long Short-Term Memory networks and subsequently extracting features with convolution operators. Compared with existing recursive neural models with tree structures, DSCNN does not rely on parsers and expensive phrase labeling, and thus is not restricted to sentence-level tasks. Moreover, unlike other CNN-based models that analyze sentences locally by sliding windows, our system captures both the dependency information within each sentence and relationships across sentences in the same document. Experiment results demonstrate that our approach is achieving state-of-the-art performance on several tasks, including sentiment analysis, question type classification, and subjectivity classification.

Connected through hyperedge HE-1 (induced by source paper "text classification with lexicon from preattention mechanism"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "no training required exploring random encoders for sentence classification"
    - "lifelong learning for sentiment classification"
    - "topic memory networks for short text classification"
    - "ncrf an open source neural sequence labeling toolkit"
    - "deriving machine attention from human rationales"
    - "a structured self attentive sentence embedding"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "contextual recurrent units for cloze style reading comprehension"):
  with members:
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "reasoning with memory augmented neural networks for language comprehension"
    - "text understanding with the attention sum reader network"
    - "iterative alternating neural attention for machine reading"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "words or characters fine grained gating for reading comprehension"
    - "ac blstm asymmetric convolutional bidirectional lstm networks for text classification"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "self balanced dropout"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "multichannel variable size convolution for sentence classification"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "on the inductive bias of dropout"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "recurrently controlled recurrent networks"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "densely connected bidirectional lstm with applications to sentence classification"
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "learning to generate reviews and discovering sentiment"
    - "sentence state lstm for text representation"

Connected through hyperedge HE-5 (induced by source paper "using deep learning for title based semantic subject indexing to reach competitive performance to full text"):
  with members:
    - "lstm a search space odyssey"
    - "on the state of the art of evaluation in neural language models"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "do convolutional networks need to be deep for text classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "deep extreme multi label learning"
    - "using titles vs full text as source for automated semantic document annotation"

Connected through hyperedge HE-6 (induced by source paper "densely connected bidirectional lstm with applications to sentence classification"):
  with members:
    - "improved neural relation detection for knowledge base question answering"
    - "multichannel variable size convolution for sentence classification"
    - "linguistically regularized lstms for sentiment classification"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "improved semantic representations from tree structured long short term memory networks"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-7 (induced by source paper "from random to supervised a novel dropout mechanism integrated with global information"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "a structured self attentive sentence embedding"
    - "harnessing deep neural networks with logic rules"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "a practitioners guide to transfer learning for text classification using convolutional neural networks"):
  with members:
    - "very deep convolutional networks for text classification"
    - "a large annotated corpus for learning natural language inference"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "how transferable are features in deep neural networks"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)