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: "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
  Abstract: Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an end-to-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses.

Connected through hyperedge HE-1 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "global to local memory pointer networks for task oriented dialogue"
    - "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "transformer xl attentive language models beyond a fixed length context"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "improving the transformer translation model with document level context"

Connected through hyperedge HE-2 (induced by source paper "a multi turn emotionally engaging dialog model"):
  with members:
    - "roberta a robustly optimized bert pretraining approach"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "touch your heart a tone aware chatbot for customer care on social media"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-3 (induced by source paper "emotionally aware chatbots a survey"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "sounding board a user centric and content driven social chatbot"
    - "touch your heart a tone aware chatbot for customer care on social media"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-4 (induced by source paper "empgan multi resolution interactive empathetic dialogue generation"):
  with members:
    - "affect driven dialog generation"
    - "a syntactically constrained bidirectional asynchronous approach for emotional conversation generation"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"
    - "affect lm a neural language model for customizable affective text generation"

Connected through hyperedge HE-5 (induced by source paper "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"):
  with members:
    - "deep contextualized word representations"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "supervised learning of universal sentence representations from natural language inference data"

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)