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: "double embeddings and cnn based sequence labeling for aspect extraction"
  Abstract: One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.

Connected through hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "voyageur an experiential travel search engine"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "subjective databases"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"

Connected through hyperedge HE-2 (induced by source paper "latent opinions transfer network for target oriented opinion words extraction"):
  with members:
    - "aspect based sentiment analysis with gated convolutional networks"
    - "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-3 (induced by source paper "bert post training for review reading comprehension and aspect based sentiment analysis"):
  with members:
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "comprehensive analysis of aspect term extraction methods using various text embeddings"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "advances in pre training distributed word representations"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "long term memory networks for question answering"
    - "recursive neural conditional random fields for aspect based sentiment analysis"

Connected through hyperedge HE-5 (induced by source paper "open domain targeted sentiment analysis via span based extraction and classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "transformation networks for target oriented sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "doer dual cross shared rnn for aspect term polarity co extraction"):
  with members:
    - "transformation networks for target oriented sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "improving aspect term extraction with bidirectional dependency tree representation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "interactive attention networks for aspect level sentiment classification"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-7 (induced by source paper "transferable end to end aspect based sentiment analysis with selective adversarial learning"):
  with members:
    - "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"
    - "tensorized self attention efficiently modeling pairwise and global dependencies together"
    - "adaptive semi supervised learning for cross domain sentiment classification"
    - "interactive attention networks for aspect level sentiment classification"
    - "disan directional self attention network for rnn cnn free language understanding"

Connected through hyperedge HE-8 (induced by source paper "pod positional dependency based word embedding for aspect term extraction"):
  with members:
    - "language models with transformers"
    - "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"
    - "recursive neural conditional random fields for aspect based sentiment analysis"

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)