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: "a hierarchical model of reviews for aspect based sentiment analysis"
  Abstract: Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a review's argumentative structure. Intuitively, sentences in a review build and elaborate upon each other; knowledge of the review structure and sentential context should thus inform the classification of each sentence. We demonstrate this hypothesis for the task of aspect-based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM. We show that the hierarchical model outperforms two non-hierarchical baselines, obtains results competitive with the state-of-the-art, and outperforms the state-of-the-art on five multilingual, multi-domain datasets without any hand-engineered features or external resources.

Connected through hyperedge HE-1 (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-2 (induced by source paper "aspect detection using word and char embeddings with bi lstm and crf"):
  with members:
    - "method for aspect based sentiment annotation using rhetorical analysis"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "gradual machine learning for aspect level sentiment analysis"):
  with members:
    - "transformation networks for target oriented sentiment classification"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "interactive attention networks for aspect level sentiment classification"
    - "character level question answering with attention"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "effective approaches to attention based neural machine translation"
    - "effective lstms for target dependent sentiment classification"

Connected through hyperedge HE-4 (induced by source paper "deep learning for sentiment analysis a survey"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "deep memory networks for attitude identification"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "structured neural topic models for reviews"):
  with members:
    - "implicit reparameterization gradients"
    - "learning disentangled representations of texts with application to biomedical abstracts"
    - "isolating sources of disentanglement in variational autoencoders"
    - "auto encoding total correlation explanation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "toward controlled generation of text"
    - "aspect augmented adversarial networks for domain adaptation"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-6 (induced by source paper "learning disentangled representations of texts with application to biomedical abstracts"):
  with members:
    - "aspect augmented adversarial networks for domain adaptation"
    - "disentangled representations for manipulation of sentiment in text"
    - "conditional similarity networks"
    - "disentangled representations in neural models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "skip thought vectors"
    - "neural variational inference for text processing"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "aspect based sentiment analysis with gated convolutional networks"):
  with members:
    - "language modeling with gated convolutional networks"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "improved semantic representations from tree structured long short term memory networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-8 (induced by source paper "extracting aspects hierarchies using rhetorical structure theory"):
  with members:
    - "method for aspect based sentiment annotation using rhetorical analysis"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "two pass discourse segmentation with pairing and global features"
    - "all maximal independent sets and dynamic dominance for sparse graphs"

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)