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: "touch your heart a tone aware chatbot for customer care on social media"
  Abstract: Chatbot has become an important solution to rapidly increasing customer care demands on social media in recent years. However, current work on chatbot for customer care ignores a key to impact user experience - tones. In this work, we create a novel tone-aware chatbot that generates toned responses to user requests on social media. We first conduct a formative research, in which the effects of tones are studied. Significant and various influences of different tones on user experience are uncovered in the study. With the knowledge of effects of tones, we design a deep learning based chatbot that takes tone information into account. We train our system on over 1.5 million real customer care conversations collected from Twitter. The evaluation reveals that our tone-aware chatbot generates as appropriate responses to user requests as human agents. More importantly, our chatbot is perceived to be even more empathetic than human agents.

Connected through hyperedge HE-1 (induced by source paper "if i hear you correctly building and evaluating interview chatbots with active listening skills"):
  with members:
    - "tell me about yourself using an ai powered chatbot to conduct conversational surveys"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "learning to ask neural question generation for reading comprehension"
    - "learning conversational systems that interleave task and non task content"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-2 (induced by source paper "love is as complex as math metaphor generation system for social chatbot"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "sounding board a user centric and content driven social chatbot"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-5 (induced by source paper "recent advances in conversational nlp towards the standardization of chatbot building"):
  with members:
    - "attend to you personalized image captioning with context sequence memory networks"
    - "adversarial learning for neural dialogue generation"
    - "a deep reinforcement learning chatbot"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "an attentional neural conversation model with improved specificity"
    - "sequence to sequence rnns for text summarization"

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.HC(Human-Computer Interaction)