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: "tartan a retrieval based socialbot powered by a dynamic finite state machine architecture"
  Abstract: This paper describes the Tartan conversational agent built for the 2018 Alexa Prize Competition. Tartan is a non-goal-oriented socialbot focused around providing users with an engaging and fluent casual conversation. Tartan's key features include an emphasis on structured conversation based on flexible finite-state models and an approach focused on understanding and using conversational acts. To provide engaging conversations, Tartan blends script-like yet dynamic responses with data-based generative and retrieval models. Unique to Tartan is that our dialog manager is modeled as a dynamic Finite State Machine. To our knowledge, no other conversational agent implementation has followed this specific structure.

Connected through hyperedge HE-1 (induced by source paper "augmenting non collaborative dialog systems with explicit semantic and strategic dialog history"):
  with members:
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "unsupervised dialog structure learning"
    - "a dynamic strategy coach for effective negotiation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sounding board a user centric and content driven social chatbot"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"
    - "a persona based neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "advancing the state of the art in open domain dialog systems through the alexa prize"):
  with members:
    - "slugbot developing a computational model andframework of a novel dialogue genre"
    - "achieving human parity on automatic chinese to english news translation"
    - "contextual language model adaptation for conversational agents"
    - "alquist the alexa prize socialbot"
    - "topic based evaluation for conversational bots"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "conversational ai the science behind the alexa prize"
    - "just ask building an architecture for extensible self service spoken language understanding"

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)