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 deep memory network"
  Abstract: We introduce a deep memory network for aspect level sentiment classification. Unlike feature-based SVM and sequential neural models such as LSTM, this approach explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect. Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory. Experiments on laptop and restaurant datasets demonstrate that our approach performs comparable to state-of-art feature based SVM system, and substantially better than LSTM and attention-based LSTM architectures. On both datasets we show that multiple computational layers could improve the performance. Moreover, our approach is also fast. The deep memory network with 9 layers is 15 times faster than LSTM with a CPU implementation.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (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"
    - "interactive attention networks for aspect level sentiment classification"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (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-4 (induced by source paper "bert post training for review reading comprehension and aspect based sentiment analysis"):
  with members:
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-5 (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-6 (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-7 (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-8 (induced by source paper "open domain targeted sentiment analysis via span based extraction and classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "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"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"

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)