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: "combining search with structured data to create a more engaging user experience in open domain dialogue"
  Abstract: The greatest challenges in building sophisticated open-domain conversational agents arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need. In order to make coherent conversational contributions in this context, a conversational agent must be able to track the types and attributes of the entities under discussion in the conversation and know how they are related. In some cases, the agent can rely on structured information sources to help identify the relevant semantic relations and produce a turn, but in other cases, the only content available comes from search, and it may be unclear which semantic relations hold between the search results and the discourse context. A further constraint is that the system must produce its contribution to the ongoing conversation in real-time. This paper describes our experience building SlugBot for the 2017 Alexa Prize, and discusses how we leveraged search and structured data from different sources to help SlugBot produce dialogic turns and carry on conversations whose length over the semi-finals user evaluation period averaged 8:17 minutes.

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"
    - "tartan a retrieval based socialbot powered by a dynamic finite state machine architecture"
    - "sounding board a user centric and content driven social chatbot"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "a persona based neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "implicit discourse relation identification for open domain dialogues"):
  with members:
    - "advancing the state of the art in open domain dialog systems through the alexa prize"
    - "deep enhanced representation for implicit discourse relation recognition"
    - "talking to myself self dialogues as data for conversational agents"
    - "deep contextualized word representations"
    - "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "slugnerds a named entity recognition tool for open domain dialogue systems"
    - "conversational ai the science behind the alexa prize"

Connected through hyperedge HE-3 (induced by source paper "slugbot developing a computational model andframework of a novel dialogue genre"):
  with members:
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "slugnerds a named entity recognition tool for open domain dialogue systems"
    - "attention is all you need"
    - "edina building an open domain socialbot with self dialogues"
    - "personabank a corpus of personal narratives and their story intention graphs"
    - "m2d monolog to dialog generation for conversational story telling"

Connected through hyperedge HE-4 (induced by source paper "slugnerds a named entity recognition tool for open domain dialogue systems"):
  with members:
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "conversational ai the science behind the alexa prize"
    - "analysis of named entity recognition and linking for tweets"

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)