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: "towards empathetic open domain conversation models a new benchmark and dataset"
  Abstract: One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model.

Connected through hyperedge HE-1 (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"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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"
    - "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"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-3 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "topic guided variational autoencoders for text generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a multi turn emotionally engaging dialog model"):
  with members:
    - "roberta a robustly optimized bert pretraining approach"
    - "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"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-6 (induced by source paper "aloha artificial learning of human attributes for dialogue agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "bert for coreference resolution baselines and analysis"
    - "learning to speak and act in a fantasy text adventure game"
    - "keep calm and switch on preserving sentiment and fluency in semantic text exchange"
    - "game based video context dialogue"

Connected through hyperedge HE-7 (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"
    - "a hierarchical latent structure for variational conversation modeling"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-8 (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"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "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"

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)