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: "distant supervision for emotion detection using facebook reactions"
  Abstract: We exploit the Facebook reaction feature in a distant supervised fashion to train a support vector machine classifier for emotion detection, using several feature combinations and combining different Facebook pages. We test our models on existing benchmarks for emotion detection and show that employing only information that is derived completely automatically, thus without relying on any handcrafted lexicon as it's usually done, we can achieve competitive results. The results also show that there is large room for improvement, especially by gearing the collection of Facebook pages, with a view to the target domain.

Connected through hyperedge HE-1 (induced by source paper "you write like you eat stylistic variation as a predictor of social stratification"):
  with members:
    - "bleaching text abstract features for cross lingual gender prediction"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "understanding and overcoming biases in customer reviews"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
    - "computational sociolinguistics a survey"
    - "adam a method for stochastic optimization"
    - "the readability of tweets and their geographic correlation with education"

Connected through hyperedge HE-2 (induced by source paper "towards multimodal emotion recognition in german speech events in cars using transfer learning"):
  with members:
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "learning word vectors for 157 languages"
    - "iest wassa 2018 implicit emotions shared task"
    - "amobee at iest 2018 transfer learning from language models"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "emotion intensities in tweets"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-3 (induced by source paper "shared feelings understanding facebook reactions to scholarly articles"):
  with members:
    - "social emotion mining techniques for facebook posts reaction prediction"
    - "how the scientific community reacts to newly submitted preprints article downloads twitter mentions and citations"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing and validating event focused emotion corpora for german and english"):
  with members:
    - "from once upon a time to happily ever after tracking emotions in novels and fairy tales"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-5 (induced by source paper "jointly learning to detect emotions and predict facebook reactions"):
  with members:
    - "social emotion mining techniques for facebook posts reaction prediction"

Connected through hyperedge HE-6 (induced by source paper "multi label classification of user reactions in online news"):
  with members:
    - "portuguese word embeddings evaluating on word analogies and natural language tasks"

Connected through hyperedge HE-7 (induced by source paper "social emotion mining techniques for facebook posts reaction prediction"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"
    - "generating sequences with recurrent neural networks"
    - "crowdsourcing a word emotion association lexicon"
    - "thumbs up sentiment classification using machine learning techniques"

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)