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: "assessing state of the art sentiment models on state of the art sentiment datasets"
  Abstract: There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets. In some papers, however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across different tasks and datasets. In this paper, we contribute to this situation by comparing several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class). We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i. e., with more than two classes). Incorporating sentiment information into word embeddings during training gives good results for datasets that are lexically similar to the training data. With our experiments, we contribute to a better understanding of the performance of different model architectures on different data sets. Consequently, we detect novel state-of-the-art results on the SenTube datasets.

Connected through hyperedge HE-1 (induced by source paper "unsupervised transfer learning via bert neuron selection"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "revealing the dark secrets of bert"
    - "linguistic knowledge and transferability of contextual representations"
    - "meta multi task learning for sequence modeling"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "improving sentiment analysis with multi task learning of negation"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "a multi sentiment resource enhanced attention network for sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer and multi task learning for noun noun compound interpretation"
    - "universal language model fine tuning for text classification"
    - "self attention a better building block for sentiment analysis neural network classifiers"

Connected through hyperedge HE-3 (induced by source paper "you shall know a user by the company it keeps dynamic representations for social media users in nlp"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "abusive language detection with graph convolutional networks"
    - "representing social media users for sarcasm detection"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "attention is all you need"
    - "a decomposable attention model for natural language inference"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "sentiment analysis is not solved assessing and probing sentiment classification"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reducing gender bias in abusive language detection"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-5 (induced by source paper "advancing nlp with cognitive language processing signals"):
  with members:
    - "entity recognition at first sight improving ner with eye movement information"
    - "finding syntax in human encephalography with beam search"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "leveraging cognitive features for sentiment analysis"
    - "what to do about non standard or non canonical language in nlp"
    - "improving sentence compression by learning to predict gaze"

Connected through hyperedge HE-6 (induced by source paper "a computational linguistic study of personal recovery in bipolar disorder"):
  with members:
    - "geocoding without geotags a text based approach for reddit"
    - "not just depressed bipolar disorder prediction on reddit"
    - "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
    - "multi task learning for mental health using social media text"
    - "natural language processing almost from scratch"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-7 (induced by source paper "sentimate learning to play chess through natural language processing"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "deepchess end to end deep neural network for automatic learning in chess"
    - "giraffe using deep reinforcement learning to play chess"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-8 (induced by source paper "a survey on sentiment and emotion analysis for computational literary studies"):
  with members:
    - "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "an analysis of emotion communication channels in fan fiction towards emotional storytelling"
    - "deep learning for sentiment analysis a survey"
    - "the evolution of sentiment analysis a review of research topics venues and top cited papers"
    - "wassa 2017 shared task on emotion intensity"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "distributed representations of words and phrases and their compositionality"

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)