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: "affective neural response generation"
  Abstract: Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding. Experiments show that these techniques improve the open-domain conversational prowess of encoder-decoder networks by enabling them to produce emotionally rich responses that are more interesting and natural.

Connected through hyperedge HE-1 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents"):
  with members:
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "rankme reliable human ratings for natural language generation"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "emma an emotionally intelligent personal assistant for improving wellbeing"
    - "finding good representations of emotions for text classification"
    - "sentiment adaptive end to end dialog systems"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"

Connected through hyperedge HE-3 (induced by source paper "expression analysis based on face regions in read world conditions"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "attention is all you need"
    - "affect lm a neural language model for customizable affective text generation"
    - "dual path networks"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "from facial expression recognition to interpersonal relation prediction"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-4 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-5 (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-6 (induced by source paper "goal embedded dual hierarchical model for task oriented dialogue generation"):
  with members:
    - "a repository of conversational datasets"
    - "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"
    - "simple fast accurate intent classification and slot labeling"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "global locally self attentive dialogue state tracker"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-7 (induced by source paper "consistent dialogue generation with self supervised feature learning"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "dialogue natural language inference"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-8 (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"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "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"

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)