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: "flipdial a generative model for two way visual dialogue"
  Abstract: We present FlipDial, a generative model for visual dialogue that simultaneously plays the role of both participants in a visually-grounded dialogue. Given context in the form of an image and an associated caption summarising the contents of the image, FlipDial learns both to answer questions and put forward questions, capable of generating entire sequences of dialogue (question-answer pairs) which are diverse and relevant to the image. To do this, FlipDial relies on a simple but surprisingly powerful idea: it uses convolutional neural networks (CNNs) to encode entire dialogues directly, implicitly capturing dialogue context, and conditional VAEs to learn the generative model. FlipDial outperforms the state-of-the-art baseline in the sequential answering task (1VD) on the VisDial dataset by a significant margin of 12 points in Mean Rank. We are the first to extend this paradigm to full two-way visual dialogue (2VD), where our model is capable of generating visually-grounded both questions and answers in sequence, for which we propose a set of novel evaluation measures and metrics.

Connected through hyperedge HE-1 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "ask no more deciding when to guess in referential visual dialogue"

Connected through hyperedge HE-2 (induced by source paper "reasoning visual dialogs with structural and partial observations"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning visual question answering by bootstrapping hard attention"
    - "discriminability objective for training descriptive captions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning human object interactions by graph parsing neural networks"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-3 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-4 (induced by source paper "vico word embeddings from visual co occurrences"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "learning type aware embeddings for fashion compatibility"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "event2mind commonsense inference on events intents and reactions"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-5 (induced by source paper "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"):
  with members:
    - "accelerating proposal generation network for fast face detection on mobile devices"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "dynamically hierarchy revolution dirnet for compressing recurrent neural network on mobile devices"
    - "focal loss for dense object detection"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "adversarial learning for neural dialogue generation"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"

Connected through hyperedge HE-6 (induced by source paper "improving generative visual dialog by answering diverse questions"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "visual coreference resolution in visual dialog using neural module networks"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "generating descriptions with grounded and co referenced people"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "learning to reason end to end module networks for visual question answering"
    - "visual reference resolution using attention memory for visual dialog"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-8 (induced by source paper "dgpose disentangled semi supervised deep generative models for human body analysis"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "learning feature pyramids for human pose estimation"
    - "robust imitation of diverse behaviors"
    - "deformable gans for pose based human image generation"
    - "mocogan decomposing motion and content for video generation"
    - "multi context attention for human pose estimation"
    - "disentangled person image generation"
    - "the pose knows video forecasting by generating pose futures"

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.CV(Computer Vision and Pattern Recognition)