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: "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
  Abstract: We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.

Connected through hyperedge HE-1 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "informative and controllable opinion summarization"):
  with members:
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "hierarchical transformers for multi document summarization"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "improving abstraction in text summarization"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "controlling linguistic style aspects in neural language generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "structured attention networks"
    - "learning structured text representations"
    - "get to the point summarization with pointer generator networks"
    - "neural discourse structure for text categorization"
    - "a deep reinforced model for abstractive summarization"
    - "neural network based abstract generation for opinions and arguments"

Connected through hyperedge HE-5 (induced by source paper "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"):
  with members:
    - "everything old is new again a multi view learning approach to learning using privileged information and distillation"
    - "deep learning for sentiment analysis a survey"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "learning from noisy singly labeled data"

Connected through hyperedge HE-6 (induced by source paper "unsupervised multi document opinion summarization as copycat review generation"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "generating wikipedia by summarizing long sequences"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-7 (induced by source paper "rethinking attribute representation and injection for sentiment classification"):
  with members:
    - "categorical metadata representation for customized text classification"
    - "dual memory network model for biased product review classification"
    - "translations as additional contexts for sentence classification"
    - "improving review representations with user attention and product attention for sentiment classification"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "cold start aware user and product attention for sentiment classification"
    - "extreme adaptation for personalized neural machine translation"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-8 (induced by source paper "an interactive multi task learning network for end to end aspect based sentiment analysis"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect term extraction with history attention and selective transformation"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "implicit discourse relation classification via multi task 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)