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: "aspect based sentiment analysis with gated convolutional networks"
  Abstract: Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.

Connected through hyperedge HE-1 (induced by source paper "component analysis for visual question answering architectures"):
  with members:
    - "m 2 meshed memory transformer for image captioning"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "language features matter effective language representations for vision language tasks"
    - "evalai towards better evaluation systems for ai agents"
    - "murel multimodal relational reasoning for visual question answering"
    - "deep learning using rectified linear units relu"
    - "stacked cross attention for image text matching"
    - "generalized hadamard product fusion operators for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "hybrid tiled convolutional neural networks for text sentiment classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "constructing a word similarity graph from vector based word representation for named entity recognition"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "comparative study of cnn and rnn for natural language processing"
    - "exploring different dimensions of attention for uncertainty detection"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "learning text representation using recurrent convolutional neural network with highway layers"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network"):
  with members:
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "multi labeled relation extraction with attentive capsule network"
    - "transformation networks for target oriented sentiment classification"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "dynamic routing between capsules"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "latent opinions transfer network for target oriented opinion words extraction"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "attention is all you need"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a convolutional encoder model for neural machine translation"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "exploiting bert for end to end aspect based sentiment analysis"):
  with members:
    - "syntax aware aspect level sentiment classification with graph attention networks"
    - "fine tune bert for extractive summarization"
    - "targeted sentiment classification with attentional encoder network"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "adapt or get left behind domain adaptation through bert language model finetuning for aspect target sentiment classification"
    - "aspect based sentiment classification with aspect specific graph convolutional networks"
    - "an interactive multi task learning network for end to end aspect based sentiment analysis"

Connected through hyperedge HE-6 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis"):
  with members:
    - "context aware embedding for targeted aspect based sentiment analysis"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "an interactive multi task learning network for end to end aspect based sentiment analysis"
    - "neural aspect and opinion term extraction with mined rules as weak supervision"
    - "multi task learning for multi modal emotion recognition and sentiment analysis"
    - "doer dual cross shared rnn for aspect term polarity co extraction"
    - "latent variable sentiment grammar"

Connected through hyperedge HE-7 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "unsupervised statistical machine translation"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "multi zone unit for recurrent neural networks"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "a novel aspect guided deep transition model for aspect based sentiment analysis"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "recurrent attention unit"
    - "long short term attention"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "multi head attention with disagreement regularization"

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)