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: "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
  Abstract: Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a given document with respect to a given aspect entity. While neural network architectures have been successful in predicting the overall polarity of sentences, aspect-specific sentiment analysis still remains as an open problem. In this paper, we propose a novel method for integrating aspect information into the neural model. More specifically, we incorporate aspect information into the neural model by modeling word-aspect relationships. Our novel model, \textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term. This ameliorates the flaws of other state-of-the-art models that utilize naive concatenations to model word-aspect similarity. Instead, our model adopts circular convolution and circular correlation to model the similarity between aspect and words and elegantly incorporates this within a differentiable neural attention framework. Finally, our model is end-to-end differentiable and highly related to convolution-correlation (holographic like) memories. Our proposed neural model achieves state-of-the-art performance on benchmark datasets, outperforming ATAE-LSTM by $4\%-5\%$ on average across multiple datasets.

Connected through hyperedge HE-1 (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-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"
    - "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-4 (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"
    - "interactive attention networks for aspect level sentiment classification"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-5 (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 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"

Connected through hyperedge HE-6 (induced by source paper "an interactive multi task learning network for end to end aspect based sentiment analysis"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect term extraction with history attention and selective transformation"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "implicit discourse relation classification via multi task neural networks"

Connected through hyperedge HE-7 (induced by source paper "parameterized convolutional neural networks for aspect level sentiment classification"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "on predicting geolocation of tweets using convolutional neural networks"
    - "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-8 (induced by source paper "a joint model for aspect category sentiment analysis with contextualized aspect embedding"):
  with members:
    - "aspect category detection via topic attention network"
    - "can constrained attention networks for multi aspect sentiment analysis"
    - "joint aspect and polarity classification for aspect based sentiment analysis with end to end neural networks"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "attention is all you need"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "speech recognition with deep recurrent neural networks"

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)