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: "targeted sentiment classification with attentional encoder network"
  Abstract: Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.

Connected through hyperedge HE-1 (induced by source paper "adversarial training for aspect based sentiment analysis with bert"):
  with members:
    - "adapt or get left behind domain adaptation through bert language model finetuning for aspect target sentiment classification"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "aspect term extraction with history attention and selective transformation"
    - "black box adversarial attacks with limited queries and information"
    - "attention is all you need"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-2 (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"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "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-3 (induced by source paper "selective attention based graph convolutional networks for aspect level sentiment classification"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "attention guided graph convolutional networks for relation extraction"
    - "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "graph convolution over pruned dependency trees improves relation extraction"

Connected through hyperedge HE-4 (induced by source paper "an attentive survey of attention models"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention models in graphs a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "self attentive sequential recommendation"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "adapt or get left behind domain adaptation through bert language model finetuning for aspect target sentiment classification"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-6 (induced by source paper "a multi task learning model for chinese oriented aspect polarity classification and aspect term extraction"):
  with members:
    - "adapt or get left behind domain adaptation through bert language model finetuning for aspect target sentiment classification"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "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"
    - "interactive attention networks for aspect level sentiment classification"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "effective lstms for target dependent sentiment classification"

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)