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: "evaluating quality of chatbots and intelligent conversational agents"
  Abstract: Chatbots are one class of intelligent, conversational software agents activated by natural language input (which can be in the form of text, voice, or both). They provide conversational output in response, and if commanded, can sometimes also execute tasks. Although chatbot technologies have existed since the 1960s and have influenced user interface development in games since the early 1980s, chatbots are now easier to train and implement. This is due to plentiful open source code, widely available development platforms, and implementation options via Software as a Service (SaaS). In addition to enhancing customer experiences and supporting learning, chatbots can also be used to engineer social harm - that is, to spread rumors and misinformation, or attack people for posting their thoughts and opinions online. This paper presents a literature review of quality issues and attributes as they relate to the contemporary issue of chatbot development and implementation. Finally, quality assessment approaches are reviewed, and a quality assessment method based on these attributes and the Analytic Hierarchy Process (AHP) is proposed and examined.

Connected through hyperedge HE-1 (induced by source paper "sunny and dark outside improving answer consistency in vqa through entailed question generation"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "cycle consistency for robust visual question answering"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "learning by asking questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "fooling vision and language models despite localization and attention mechanism"

Connected through hyperedge HE-2 (induced by source paper "designing for health chatbots"):
  with members:
    - "confiding in and listening to virtual agents the effect of personality"
    - "hierarchical recurrent attention network for response generation"
    - "bots as virtual confederates design and ethics"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "adversarial evaluation of dialogue models"
    - "a survey of available corpora for building data driven dialogue systems"
    - "neural responding machine for short text conversation"
    - "artificial intelligence markup language a brief tutorial"

Connected through hyperedge HE-3 (induced by source paper "how should my chatbot interact a survey on human chatbot interaction design"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "understanding chatbot mediated task management"
    - "style transfer through back translation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "can a chatbot determine my diet addressing challenges of chatbot application for meal recommendation"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "a hybrid architecture for multi party conversational systems"
    - "chatbots greetings to human computer communication"

Connected through hyperedge HE-4 (induced by source paper "conversational agents for insurance companies from theory to practice"):
  with members:
    - "motivations classification and model trial of conversational agents for insurance companies"

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.CY(Computers and Society)