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: "interactive attention networks for aspect level sentiment classification"
  Abstract: Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.

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 level sentiment classification with attention over attention neural networks"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "dynamic routing between capsules"
    - "aspect level sentiment classification with deep memory network"
    - "character level convolutional networks for text classification"

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"
    - "targeted sentiment classification with attentional encoder network"
    - "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"

Connected through hyperedge HE-3 (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-4 (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"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a systematic study of the class imbalance problem in convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "positional attention based frame identification with bert a deep learning approach to target disambiguation and semantic frame selection"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning joint semantic parsers from disjoint data"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"

Connected through hyperedge HE-6 (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"
    - "targeted sentiment classification with attentional encoder network"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "doer dual cross shared rnn for aspect term polarity co extraction"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "improving aspect term extraction with bidirectional dependency tree representation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-8 (induced by source paper "tdam a topic dependent attention model for sentiment analysis"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "learning structured text representations"
    - "attention is all you need"
    - "the consciousness prior"
    - "adversarial multi task learning for text 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.AI(Artificial Intelligence)