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: "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
  Abstract: While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects --- remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external "memory chains" with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those using external knowledge bases.

Connected through hyperedge HE-1 (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"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "transfer learning between related tasks using expected label proportions"):
  with members:
    - "exploiting document knowledge for aspect level sentiment classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "cross lingual pseudo projected expectation regularization for weakly supervised learning"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aspect level sentiment classification with deep memory network"
    - "sentihood targeted aspect based sentiment analysis dataset for urban neighbourhoods"
    - "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"
    - "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"
    - "sentihood targeted aspect based sentiment analysis dataset for urban neighbourhoods"

Connected through hyperedge HE-6 (induced by source paper "context aware embedding for targeted aspect based sentiment analysis"):
  with members:
    - "aspect level sentiment classification with deep memory network"
    - "sentihood targeted aspect based sentiment analysis dataset for urban neighbourhoods"

Connected through hyperedge HE-7 (induced by source paper "variational semi supervised aspect term sentiment analysis via transformer"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "transformation networks for target oriented sentiment classification"
    - "deep contextualized word representations"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "interactive attention networks for aspect level sentiment classification"

Connected through hyperedge HE-8 (induced by source paper "evaluating the utility of hand crafted features in sequence labelling"):
  with members:
    - "narrative modeling with memory chains and semantic supervision"
    - "deep contextualized word representations"
    - "neural reranking for named entity recognition"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

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)