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: "emulating human conversations using convolutional neural network based ir"
  Abstract: Conversational agents ("bots") are beginning to be widely used in conversational interfaces. To design a system that is capable of emulating human-like interactions, a conversational layer that can serve as a fabric for chat-like interaction with the agent is needed. In this paper, we introduce a model that employs Information Retrieval by utilizing convolutional deep structured semantic neural network-based features in the ranker to present human-like responses in ongoing conversation with a user. In conversations, accounting for context is critical to the retrieval model; we show that our context-sensitive approach using a Convolutional Deep Structured Semantic Model (cDSSM) with character trigrams significantly outperforms several conventional baselines in terms of the relevance of responses retrieved.

Connected through hyperedge HE-1 (induced by source paper "a statistical defense approach for detecting adversarial examples"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "adversarial diversity and hard positive generation"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "learning with a strong adversary"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "ruuh a deep learning based conversational social agent"):
  with members:
    - "a sentiment and semantics based approach for emotion detection in textual conversations"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-3 (induced by source paper "not all dialogues are created equal instance weighting for neural conversational models"):
  with members:
    - "a knowledge grounded neural conversation model"
    - "adversarial learning for neural dialogue generation"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

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.AI(Artificial Intelligence)