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: "deep learning for sentiment analysis a survey"
  Abstract: Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

Connected through hyperedge HE-1 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affective computing for large scale heterogeneous multimedia data a survey"
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "human centered emotion recognition in animated gifs"
    - "a multi task neural approach for emotion attribution classification and summarization"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-2 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "detection of the prodromal phase of bipolar disorder from psychological and phonological aspects in social media"
    - "advances in pre training distributed word representations"
    - "enriching word vectors with subword information"
    - "identifying depression on twitter"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-3 (induced by source paper "constructing a highlight classifier with an attention based lstm neural network"):
  with members:
    - "personalized neural embeddings for collaborative filtering with text"
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "generalizing word embeddings using bag of subwords"
    - "word mover s embedding from word2vec to document embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automatic music highlight extraction using convolutional recurrent attention networks"
    - "attention is all you need"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-4 (induced by source paper "a bert based sentiment analysis and key entity detection approach for online financial texts"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "ernie enhanced language representation with informative entities"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "albert a lite bert for self supervised learning of language representations"
    - "cross entropy adversarial view adaptation for person re identification"
    - "pre training with whole word masking for chinese bert"
    - "roberta a robustly optimized bert pretraining approach"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "towards quantifying the distance between opinions"):
  with members:
    - "viewpoint discovery and understanding in social networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "globally normalized transition based neural networks"
    - "distributed representations of sentences and documents"
    - "opinion mining and analysis a survey"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "fpga based accelerators of deep learning networks for learning and classification a review"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"
    - "hardware for machine learning challenges and opportunities"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "parallel multi channel convolution using general matrix multiplication"
    - "ese efficient speech recognition engine with sparse lstm on fpga"

Connected through hyperedge HE-8 (induced by source paper "word class embeddings for multiclass text classification"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "enhancing domain word embedding via latent semantic imputation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"
    - "attention is all you need"

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)