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: "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
  Abstract: Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-2 (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"
    - "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"
    - "combining mixmatch and active learning for better accuracy with fewer labels"

Connected through hyperedge HE-3 (induced by source paper "hierarchical transformer network for utterance level emotion recognition"):
  with members:
    - "pt code pre trained context dependent encoder for utterance level emotion recognition"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "utilizing bert intermediate layers for aspect based sentiment analysis and natural language inference"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"

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"
    - "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"
    - "doer dual cross shared rnn for aspect term polarity co extraction"

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"
    - "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"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

Connected through hyperedge HE-7 (induced by source paper "syntax aware aspect level sentiment classification with graph attention networks"):
  with members:
    - "residual or gate towards deeper graph neural networks for inductive graph representation learning"
    - "fast graph representation learning with pytorch geometric"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "a graph based model for joint chinese word segmentation and dependency parsing"):
  with members:
    - "searching for effective neural extractive summarization what works and what s next"
    - "pre training with whole word masking for chinese bert"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "decoupled weight decay regularization"
    - "joint pos tagging and dependency parsing with transition based 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)