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: "top down visual saliency guided by captions"
  Abstract: Neural image/video captioning models can generate accurate descriptions, but their internal process of mapping regions to words is a black box and therefore difficult to explain. Top-down neural saliency methods can find important regions given a high-level semantic task such as object classification, but cannot use a natural language sentence as the top-down input for the task. In this paper, we propose Caption-Guided Visual Saliency to expose the region-to-word mapping in modern encoder-decoder networks and demonstrate that it is learned implicitly from caption training data, without any pixel-level annotations. Our approach can produce spatial or spatiotemporal heatmaps for both predicted captions, and for arbitrary query sentences. It recovers saliency without the overhead of introducing explicit attention layers, and can be used to analyze a variety of existing model architectures and improve their design. Evaluation on large-scale video and image datasets demonstrates that our approach achieves comparable captioning performance with existing methods while providing more accurate saliency heatmaps. Our code is available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "combined task and action learning from human demonstrations for mobile manipulation applications"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "integrating knowledge and reasoning in image understanding"
    - "improving grounded natural language understanding through human robot dialog"
    - "learning actions from human demonstration video for robotic manipulation"
    - "prospection interpretable plans from language by predicting the future"

Connected through hyperedge HE-2 (induced by source paper "multi level bottom top and top bottom feature fusion for crowd counting"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "learning from synthetic data for crowd counting in the wild"
    - "uncertainty guided multi scale residual learning using a cycle spinning cnn for single image de raining"
    - "deeply supervised salient object detection with short connections"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-3 (induced by source paper "scene understanding for autonomous manipulation with deep learning"):
  with members:
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "visual affordance and function understanding a survey"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-4 (induced by source paper "a semantics assisted video captioning model trained with scheduled sampling"):
  with members:
    - "videobert a joint model for video and language representation learning"
    - "less is more picking informative frames for video captioning"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "exploring visual relationship for image captioning"
    - "reinforced video captioning with entailment rewards"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-5 (induced by source paper "saliency prediction on omnidirectional images with generative adversarial imitation learning"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "deep visual attention prediction"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "salnet360 saliency maps for omni directional images with cnn"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-6 (induced by source paper "semi supervised phrase localization in a bidirectional caption image retrieval framework"):
  with members:
    - "learning unsupervised visual grounding through semantic self supervision"
    - "multimodal machine learning a survey and taxonomy"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "an end to end approach to natural language object retrieval via context aware deep reinforcement learning"
    - "deep metric learning via facility location"
    - "grounding of textual phrases in images by reconstruction"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-7 (induced by source paper "visual discourse parsing"):
  with members:
    - "multi task video captioning with video and entailment generation"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "video paragraph captioning using hierarchical recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "multi level multimodal common semantic space for image phrase grounding"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "grounding visual explanations"
    - "learning unsupervised visual grounding through semantic self supervision"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

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)