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: "talking to myself self dialogues as data for conversational agents"
  Abstract: Conversational agents are gaining popularity with the increasing ubiquity of smart devices. However, training agents in a data driven manner is challenging due to a lack of suitable corpora. This paper presents a novel method for gathering topical, unstructured conversational data in an efficient way: self-dialogues through crowd-sourcing. Alongside this paper, we include a corpus of 3.6 million words across 23 topics. We argue the utility of the corpus by comparing self-dialogues with standard two-party conversations as well as data from other corpora.

Connected through hyperedge HE-1 (induced by source paper "beyond turing intelligent agents centered on the user"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "leveraging user engagement signals for entity labeling in a virtual assistant"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "a multimodal lstm for predicting listener empathic responses over time"
    - "conversational ai the science behind the alexa prize"
    - "laying down the yellow brick road development of a wizard of oz interface for collecting human robot dialogue"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-2 (induced by source paper "effective data augmentation approaches to end to end task oriented dialogue"):
  with members:
    - "adversarial over sensitivity and over stability strategies for dialogue models"
    - "automatic data expansion for customer care spoken language understanding"
    - "sequence to sequence data augmentation for dialogue language understanding"
    - "key value retrieval networks for task oriented dialogue"
    - "latent intention dialogue models"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"

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)