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: "online sequence to sequence active learning for open domain dialogue generation"
  Abstract: We propose an online, end-to-end, neural generative conversational model for open-domain dialog. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on a diversity-promoting heuristic for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of meaningful, relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.

Connected through hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation"):
  with members:
    - "avgout a simple output probability measure to eliminate dull responses"
    - "towards a human like open domain chatbot"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "comparison of diverse decoding methods from conditional language models"
    - "fast structured decoding for sequence models"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "pnat non autoregressive transformer by position learning"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-2 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-3 (induced by source paper "diversifying topic coherent response generation for natural multi turn conversations"):
  with members:
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "multi level memory for task oriented dialogs"
    - "variational memory encoder decoder"
    - "language gans falling short"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "diverse beam search for increased novelty in abstractive summarization"
    - "toward controlled generation of text"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-4 (induced by source paper "an active information seeking model for goal oriented vision and language tasks"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "inferring and executing programs for visual reasoning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "a simple neural network module for relational reasoning"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stein variational policy gradient"

Connected through hyperedge HE-5 (induced by source paper "what s to know uncertainty as a guide to asking goal oriented questions"):
  with members:
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "adversarial learning for neural dialogue generation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "deep reinforcement learning for dialogue generation"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "continuously learning neural dialogue management"

Connected through hyperedge HE-6 (induced by source paper "explore exploit a framework for interactive and online learning"):
  with members:
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "neural architecture search with reinforcement learning"
    - "algorithms for multi armed bandit problems"
    - "exploration in interactive personalized music recommendation a reinforcement learning approach"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-7 (induced by source paper "are you talking to me reasoned visual dialog generation through adversarial learning"):
  with members:
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "adversarial learning for neural dialogue generation"
    - "visual reference resolution using attention memory for visual dialog"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-8 (induced by source paper "adversarial learning for neural dialogue generation"):
  with members:
    - "aspect augmented adversarial networks for domain adaptation"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to sequence learning as beam search optimization"
    - "a simple fast diverse decoding algorithm for neural generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "seqgan sequence generative adversarial nets with policy gradient"

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)