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: "a sentiment and semantics based approach for emotion detection in textual conversations"
  Abstract: Emotions are physiological states generated in humans in reaction to internal or external events. They are complex and studied across numerous fields including computer science. As humans, on reading "Why don't you ever text me!" we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. Lack of facial expressions and voice modulations make detecting emotions from text a challenging problem. However, as humans increasingly communicate using text messaging applications, and digital agents gain popularity in our society, it is essential that these digital agents are emotion aware, and respond accordingly. #R##N#In this paper, we propose a novel approach to detect emotions like happy, sad or angry in textual conversations using an LSTM based Deep Learning model. Our approach consists of semi-automated techniques to gather training data for our model. We exploit advantages of semantic and sentiment based embeddings and propose a solution combining both. Our work is evaluated on real-world conversations and significantly outperforms traditional Machine Learning baselines as well as other off-the-shelf Deep Learning models.

Connected through hyperedge HE-1 (induced by source paper "a transformer based approach to irony and sarcasm detection"):
  with members:
    - "semeval 2014 task 9 sentiment analysis in twitter"
    - "spanbert improving pre training by representing and predicting spans"
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "a survey on sentiment and emotion analysis for computational literary studies"
    - "deep contextualized word representations for detecting sarcasm and irony"

Connected through hyperedge HE-2 (induced by source paper "gwu nlp lab at semeval 2019 task 3 emocontext effective contextual information in models for emotion detection in sentence level in a multigenre corpus"):
  with members:
    - "deep contextualized word representations"
    - "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"
    - "wassa 2017 shared task on emotion intensity"
    - "advances in pre training distributed word representations"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "gated feedback recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "emotion detection with neural personal discrimination"):
  with members:
    - "attention is all you need"
    - "long short term memory networks for machine reading"
    - "enhanced twitter sentiment classification using contextual information"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "towards empathetic open domain conversation models a new benchmark and dataset"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "multi task deep neural networks for natural language understanding"
    - "learning semantic textual similarity from conversations"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "training millions of personalized dialogue agents"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (induced by source paper "ruuh a deep learning based conversational social agent"):
  with members:
    - "emulating human conversations using convolutional neural network based ir"
    - "from captions to visual concepts and back"

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)