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: "molding cnns for text non linear non consecutive convolutions"
  Abstract: The success of deep learning often derives from well-chosen operational building blocks. In this work, we revise the temporal convolution operation in CNNs to better adapt it to text processing. Instead of concatenating word representations, we appeal to tensor algebra and use low-rank n-gram tensors to directly exploit interactions between words already at the convolution stage. Moreover, we extend the n-gram convolution to non-consecutive words to recognize patterns with intervening words. Through a combination of low-rank tensors, and pattern weighting, we can efficiently evaluate the resulting convolution operation via dynamic programming. We test the resulting architecture on standard sentiment classification and news categorization tasks. Our model achieves state-of-the-art performance both in terms of accuracy and training speed. For instance, we obtain 51.2% accuracy on the fine-grained sentiment classification task.

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"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "multi scale self attention for text classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "star transformer"
    - "sequence labeling a practical approach"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "low rank hoca efficient high order cross modal attention for video captioning"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "reconstruction network for video captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video captioning via hierarchical reinforcement learning"
    - "weakly supervised dense video captioning"
    - "video captioning with guidance of multimodal latent topics"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "pod positional dependency based word embedding for aspect term extraction"):
  with members:
    - "language models with transformers"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "aspect term extraction with history attention and selective transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "elixa a modular and flexible absa platform"

Connected through hyperedge HE-5 (induced by source paper "ranking sentences for extractive summarization with reinforcement learning"):
  with members:
    - "graph based neural multi document summarization"
    - "neural extractive summarization with side information"
    - "deep reinforcement learning for dialogue generation"
    - "neural summarization by extracting sentences and words"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-6 (induced by source paper "improving neural sequence labelling using additional linguistic information"):
  with members:
    - "semi supervised multitask learning for sequence labeling"
    - "a general purpose tagger with convolutional neural networks"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "neural architectures for named entity recognition"
    - "linguistic input features improve neural machine translation"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-7 (induced by source paper "sopa bridging cnns rnns and weighted finite state machines"):
  with members:
    - "recurrent additive networks"
    - "recurrent neural networks as weighted language recognizers"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "convolutional sequence to sequence learning"
    - "quasi recurrent neural networks"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through representation erasure"

Connected through hyperedge HE-8 (induced by source paper "a study of question effectiveness using reddit ask me anything threads"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "a neural attention model for abstractive sentence summarization"
    - "show attend and tell neural image caption generation with visual attention"
    - "talking to the crowd what do people react to in online discussions"
    - "sequence to sequence learning with neural networks"

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)