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: "mojitalk generating emotional responses at scale"
  Abstract: Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels. Additionally, explicitly controlling the emotion and sentiment of generated text is also difficult. In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis. More specifically, we collect a large corpus of Twitter conversations that include emojis in the response, and assume the emojis convey the underlying emotions of the sentence. We then introduce a reinforced conditional variational encoder approach to train a deep generative model on these conversations, which allows us to use emojis to control the emotion of the generated text. Experimentally, we show in our quantitative and qualitative analyses that the proposed models can successfully generate high-quality abstractive conversation responses in accordance with designated emotions.

Connected through hyperedge HE-1 (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"
    - "affect lm a neural language model for customizable affective text generation"

Connected through hyperedge HE-2 (induced by source paper "emptransfo a multi head transformer architecture for creating empathetic dialog systems"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "delivering cognitive behavioral therapy using a conversational socialrobot"
    - "huggingface s transformers state of the art natural language processing"
    - "affect driven dialog generation"
    - "bleu is not suitable for the evaluation of text simplification"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-3 (induced by source paper "moel mixture of empathetic listeners"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "skeleton to response dialogue generation guided by retrieval memory"

Connected through hyperedge HE-4 (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"
    - "attention is all you need"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-5 (induced by source paper "multivariate information adversarial ensemble for scalable joint distribution matching"):
  with members:
    - "video to video synthesis"
    - "geometry score a method for comparing generative adversarial networks"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "soft gated warping gan for pose guided person image synthesis"
    - "modular generative adversarial networks"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "style transfer from non parallel text by cross alignment"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "memory augmented recurrent networks for dialogue coherence"):
  with members:
    - "towards universal dialogue state tracking"
    - "a hierarchical latent structure for variational conversation modeling"
    - "deep contextualized word representations"
    - "memory augmented neural networks with wormhole connections"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "pointing the unknown words"
    - "a persona based neural conversation model"
    - "dynamic neural turing machine with soft and hard addressing schemes"

Connected through hyperedge HE-7 (induced by source paper "an adversarial approach to high quality sentiment controlled neural dialogue generation"):
  with members:
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "adversarial learning for neural dialogue generation"
    - "a conditional variational framework for dialog generation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-8 (induced by source paper "approximating interactive human evaluation with self play for open domain dialog systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "unifying human and statistical evaluation for natural language generation"
    - "hierarchical reinforcement learning for open domain dialog"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "a hierarchical latent structure for variational conversation modeling"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "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)