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: "aspect level sentiment classification with attention over attention neural networks"
  Abstract: Aspect-level sentiment classification aims to identify the sentiment expressed towards some aspects given context sentences. In this paper, we introduce an attention-over-attention (AOA) neural network for aspect level sentiment classification. Our approach models aspects and sentences in a joint way and explicitly captures the interaction between aspects and context sentences. With the AOA module, our model jointly learns the representations for aspects and sentences, and automatically focuses on the important parts in sentences. Our experiments on laptop and restaurant datasets demonstrate our approach outperforms previous LSTM-based architectures.

Connected through hyperedge HE-1 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network"):
  with members:
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "multi labeled relation extraction with attentive capsule network"
    - "transformation networks for target oriented sentiment classification"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "dynamic routing between capsules"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (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"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a failure of aspect sentiment classifiers and an adaptive re weighting solution"):
  with members:
    - "review conversational reading comprehension"
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "the impact of class rebalancing techniques on the performance and interpretation of defect prediction models"
    - "transformation networks for target oriented sentiment classification"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "syntax aware aspect level sentiment classification with graph attention networks"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "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 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-5 (induced by source paper "aspect based sentiment classification with aspect specific graph convolutional networks"):
  with members:
    - "dating documents using graph convolution networks"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "transformation networks for target oriented sentiment classification"
    - "graph convolutional networks for text classification"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"

Connected through hyperedge HE-6 (induced by source paper "parameterized convolutional neural networks for aspect level sentiment classification"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "on predicting geolocation of tweets using convolutional neural networks"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "language modeling with gated convolutional networks"
    - "end to end memory networks"
    - "effective lstms for target dependent sentiment classification"

Connected through hyperedge HE-7 (induced by source paper "gradual machine learning for aspect level sentiment analysis"):
  with members:
    - "transformation networks for target oriented sentiment classification"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "interactive attention networks for aspect level sentiment classification"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "character level question answering with attention"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "effective approaches to attention based neural machine translation"
    - "effective lstms for target dependent sentiment classification"

Connected through hyperedge HE-8 (induced by source paper "a multi task learning model for chinese oriented aspect polarity classification and aspect term extraction"):
  with members:
    - "targeted sentiment classification with attentional encoder network"
    - "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 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)