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: "adaptive feature abstraction for translating video to text"
  Abstract: Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent semantics in the video may make it more appropriate to adaptively select features from the multiple CNN layers. We propose a new approach for generating adaptive spatiotemporal representations of videos for the captioning task. A novel attention mechanism is developed, that adaptively and sequentially focuses on different layers of CNN features (levels of feature "abstraction"), as well as local spatiotemporal regions of the feature maps at each layer. The proposed approach is evaluated on three benchmark datasets: YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the model works, these experiments quantitatively demonstrate the effectiveness of the proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantics.

Connected through hyperedge HE-1 (induced by source paper "disentangled deep autoencoding regularization for robust image classification"):
  with members:
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "unsupervised disentangled representation learning with analogical relations"
    - "learning to decompose and disentangle representations for video prediction"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "decomposing motion and content for natural video sequence prediction"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "unsupervised learning of disentangled representations from video"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-2 (induced by source paper "gated hierarchical attention for image captioning"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "dual recurrent attention units for visual question answering"
    - "learning to evaluate image captioning"
    - "learn to pay attention"
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "convolutional image captioning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-3 (induced by source paper "end to end video captioning with multitask reinforcement learning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition"

Connected through hyperedge HE-4 (induced by source paper "hierarchically structured reinforcement learning for topically coherent visual story generation"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "reinforced video captioning with entailment rewards"
    - "improved image captioning via policy gradient optimization of spider"
    - "video captioning via hierarchical reinforcement learning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "dense captioning events in videos"
    - "hierarchically attentive rnn for album summarization and storytelling"

Connected through hyperedge HE-5 (induced by source paper "a dual stage attention based recurrent neural network for time series prediction"):
  with members:
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "integrated pre processing for bayesian nonlinear system identification with gaussian processes"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "semantic compositional networks for visual captioning"):
  with members:
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "attention correctness in neural image captioning"
    - "rich image captioning in the wild"
    - "adaptive feature abstraction for translating video to language"
    - "on multiplicative integration with recurrent neural networks"
    - "learning attributes equals multi source domain generalization"
    - "jointly modeling embedding and translation to bridge video and language"

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)