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: "sentihood targeted aspect based sentiment analysis dataset for urban neighbourhoods"
  Abstract: In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighbourhoods are discussed by users. In this context units of text often mention several aspects of one or more neighbourhoods. This is the first time that a generic social media platform in this case a QA platform, is used for fine-grained opinion mining. Text coming from QA platforms is far less constrained compared to text from review specific platforms which current datasets are based on. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks.

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 "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"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-3 (induced by source paper "pars absa an aspect based sentiment analysis dataset in persian"):
  with members:
    - "aspect level sentiment classification with attention over attention neural networks"
    - "sentipers a sentiment analysis corpus for persian"
    - "character level convolutional networks for text classification"
    - "effective lstms for target dependent sentiment classification"
    - "learning document level semantic properties from free text annotations"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-4 (induced by source paper "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"):
  with members:
    - "deep contextualized word representations"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aspect level sentiment classification with deep memory network"
    - "tracking the world state with recurrent entity networks"
    - "effective lstms for target dependent sentiment classification"

Connected through hyperedge HE-5 (induced by source paper "targeted sentiment analysis a data driven categorization"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "deep learning for sentiment analysis a survey"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "aspect level sentiment classification with deep memory network"
    - "unsupervised word and dependency path embeddings for aspect term extraction"

Connected through hyperedge HE-6 (induced by source paper "context aware embedding for targeted aspect based sentiment analysis"):
  with members:
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect level sentiment classification with deep memory network"

Connected through hyperedge HE-7 (induced by source paper "when and where behavior dominant location forecasting with micro blog streams"):
  with members:
    - "distributed representations of sentences and documents"
    - "asymptotically exact streaming algorithms"
    - "a data driven study of influences in twitter communities"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"):
  with members:
    - "tracking the world state with recurrent entity networks"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)