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: "regularizing rnns for caption generation by reconstructing the past with the present"
  Abstract: Recently, caption generation with an encoder-decoder framework has been extensively studied and applied in different domains, such as image captioning, code captioning, and so on. In this paper, we propose a novel architecture, namely Auto-Reconstructor Network (ARNet), which, coupling with the conventional encoder-decoder framework, works in an end-to-end fashion to generate captions. ARNet aims at reconstructing the previous hidden state with the present one, besides behaving as the input-dependent transition operator. Therefore, ARNet encourages the current hidden state to embed more information from the previous one, which can help regularize the transition dynamics of recurrent neural networks (RNNs). Extensive experimental results show that our proposed ARNet boosts the performance over the existing encoder-decoder models on both image captioning and source code captioning tasks. Additionally, ARNet remarkably reduces the discrepancy between training and inference processes for caption generation. Furthermore, the performance on permuted sequential MNIST demonstrates that ARNet can effectively regularize RNN, especially on modeling long-term dependencies. Our code is available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-2 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"
    - "dual supervised learning"

Connected through hyperedge HE-3 (induced by source paper "reflective decoding network for image captioning"):
  with members:
    - "neural baby talk"
    - "mattnet modular attention network for referring expression comprehension"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "self attentive residual decoder for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "controllable video captioning with pos sequence guidance based on gated fusion network"):
  with members:
    - "reconstruct and represent video contents for captioning via reinforcement learning"
    - "hierarchical photo scene encoder for album storytelling"
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "unsupervised image captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-5 (induced by source paper "pr product a substitute for inner product in neural networks"):
  with members:
    - "neural baby talk"
    - "context aware visual policy network for sequence level image captioning"
    - "recurrent fusion network for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-6 (induced by source paper "scene based factored attention for image captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "learning to guide decoding for image captioning"
    - "discriminability objective for training descriptive captions"
    - "improved image captioning via policy gradient optimization of spider"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "locally supervised deep hybrid model for scene recognition"

Connected through hyperedge HE-7 (induced by source paper "spatio temporal video re localization by warp lstm"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "a better baseline for ava"
    - "video re localization"
    - "actor centric relation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-8 (induced by source paper "learning to caption images with two stream attention and sentence auto encoder"):
  with members:
    - "improving image captioning by leveraging knowledge graphs"
    - "attention on attention for image captioning"
    - "auto encoding scene graphs for image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "convolutional sequence to sequence learning"
    - "sequence to sequence learning as beam search optimization"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "densely connected convolutional networks"

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)