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: "a hybrid architecture for multi party conversational systems"
  Abstract: Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architecture using both rules and machine learning and some insights after implementing and evaluating one on the finance domain.

Connected through hyperedge HE-1 (induced by source paper "incorporating interlocutor aware context into response generation on multi party chatbots"):
  with members:
    - "addressee and response selection in multi party conversations with speaker interaction rnns"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"
    - "a persona based neural conversation model"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-2 (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"
    - "evaluating quality of chatbots and intelligent conversational agents"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "chatbots greetings to human computer communication"

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)