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: "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
  Abstract: Automatic generation of video captions is a fundamental challenge in computer vision. Recent techniques typically employ a combination of Convolutional Neural Networks (CNNs) and Recursive Neural Networks (RNNs) for video captioning. These methods mainly focus on tailoring sequence learning through RNNs for better caption generation, whereas off-the-shelf visual features are borrowed from CNNs. We argue that careful designing of visual features for this task is equally important, and present a visual feature encoding technique to generate semantically rich captions using Gated Recurrent Units (GRUs). Our method embeds rich temporal dynamics in visual features by hierarchically applying Short Fourier Transform to CNN features of the whole video. It additionally derives high level semantics from an object detector to enrich the representation with spatial dynamics of the detected objects. The final representation is projected to a compact space and fed to a language model. By learning a relatively simple language model comprising two GRU layers, we establish new state-of-the-art on MSVD and MSR-VTT datasets for METEOR and ROUGE_L metrics.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"

Connected through hyperedge HE-2 (induced by source paper "object relational graph with teacher recommended learning for video captioning"):
  with members:
    - "controllable video captioning with pos sequence guidance based on gated fusion network"
    - "memory attended recurrent network for video captioning"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "relation aware graph attention network for visual question answering"
    - "less is more picking informative frames for video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "videos as space time region graphs"

Connected through hyperedge HE-3 (induced by source paper "delving deeper into the decoder for video captioning"):
  with members:
    - "a semantics assisted video captioning model trained with scheduled sampling"
    - "a hierarchical reinforced sequence operation method for unsupervised text style transfer"
    - "eco efficient convolutional network for online video understanding"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reinforced video captioning with entailment rewards"
    - "attend and interact higher order object interactions for video understanding"
    - "video captioning via hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "multimodal transformer with pointer network for the dstc8 avsd challenge"):
  with members:
    - "multimodal transformer networks for end to end video grounded dialogue systems"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "motion appearance co memory networks for video question answering"
    - "tvqa localized compositional video question answering"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-5 (induced by source paper "vision and language from visual perception to content creation"):
  with members:
    - "hierarchy parsing for image captioning"
    - "unsupervised person image generation with semantic parsing transformation"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "generating multiple objects at spatially distinct locations"
    - "joint discriminative and generative learning for person re identification"
    - "relationship aware spatial perception fusion for realistic scene layout generation"
    - "photo realistic facial details synthesis from single image"
    - "face to parameter translation for game character auto creation"

Connected through hyperedge HE-6 (induced by source paper "diverse video captioning through latent variable expansion with conditional gan"):
  with members:
    - "describing like humans on diversity in image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reconstruction network for video captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "convolutional image captioning"
    - "weakly supervised dense video captioning"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-7 (induced by source paper "relational reasoning using prior knowledge for visual captioning"):
  with members:
    - "improving image captioning by leveraging knowledge graphs"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning visual knowledge memory networks for visual question answering"
    - "object hallucination in image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-8 (induced by source paper "empirical autopsy of deep video captioning frameworks"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "dense captioning events in videos"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "re evaluating automatic metrics for image captioning"
    - "enriching word vectors with subword information"
    - "beyond caption to narrative video captioning with multiple sentences"

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)