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: "frame and segment level features and candidate pool evaluation for video caption generation"
  Abstract: We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset. Our model is based on the encoder--decoder pipeline, popular in image and video captioning systems. We propose to utilize two different kinds of video features, one to capture the video content in terms of objects and attributes, and the other to capture the motion and action information. Using these diverse features we train models specializing in two separate input sub-domains. We then train an evaluator model which is used to pick the best caption from the pool of candidates generated by these domain expert models. We argue that this approach is better suited for the current video captioning task, compared to using a single model, due to the diversity in the dataset. #R##N#Efficacy of our method is proven by the fact that it was rated best in MSR Video to Language Challenge, as per human evaluation. Additionally, we were ranked second in the automatic evaluation metrics based table.

Connected through hyperedge HE-1 (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"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "memory attended recurrent network for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "automatic stance detection using end to end memory networks"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-3 (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"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "unsupervised image captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "memory augmented attention modelling for videos"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "video captioning with transferred semantic attributes"
    - "recurrent memory addressing for describing videos"
    - "semantic compositional networks for visual captioning"

Connected through hyperedge HE-5 (induced by source paper "diverse video captioning through latent variable expansion with conditional gan"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "video captioning with text based dynamic attention and step by step learning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "memory attended recurrent network for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "reconstruction network for video captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "saliency guided video classification via adaptively weighted learning"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-7 (induced by source paper "characterizing the impact of using features extracted from pre trained models on the quality of video captioning sequence to sequence models"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning social image embedding with deep multimodal attention networks"
    - "video captioning with transferred semantic attributes"
    - "video captioning with multi faceted attention"
    - "soundnet learning sound representations from unlabeled video"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"
    - "microsoft coco captions data collection and evaluation server"

Connected through hyperedge HE-8 (induced by source paper "adversarial inference for multi sentence video description"):
  with members:
    - "video to video synthesis"
    - "grounding visual explanations"
    - "less is more picking informative frames for video captioning"
    - "improving image captioning with conditional generative adversarial nets"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "improving reinforcement learning based image captioning with natural language prior"
    - "learning to write with cooperative discriminators"
    - "bidirectional attentive fusion with context gating for dense video captioning"

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)