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: "making 360 circ video watchable in 2d learning videography for click free viewing"
  Abstract: 360$^{\circ}$ video requires human viewers to actively control "where" to look while watching the video. Although it provides a more immersive experience of the visual content, it also introduces additional burden for viewers; awkward interfaces to navigate the video lead to suboptimal viewing experiences. Virtual cinematography is an appealing direction to remedy these problems, but conventional methods are limited to virtual environments or rely on hand-crafted heuristics. We propose a new algorithm for virtual cinematography that automatically controls a virtual camera within a 360$^{\circ}$ video. Compared to the state of the art, our algorithm allows more general camera control, avoids redundant outputs, and extracts its output videos substantially more efficiently. Experimental results on over 7 hours of real "in the wild" video show that our generalized camera control is crucial for viewing 360$^{\circ}$ video, while the proposed efficient algorithm is essential for making the generalized control computationally tractable.

Connected through hyperedge HE-1 (induced by source paper "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "yolov3 an incremental improvement"
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "learning so 3 equivariant representations with spherical cnns"
    - "self view grounding given a narrated 360 deg video"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "revisiting deep architectures for head motion prediction in 360 videos"):
  with members:
    - "spherical clustering of users navigating 360 degree content"
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "rate utility optimized streaming of volumetric media for augmented reality"
    - "predicting deeper into the future of semantic segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "on human motion prediction using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "kernel transformer networks for compact spherical convolution"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "active convolution learning the shape of convolution for image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"

Connected through hyperedge HE-4 (induced by source paper "cube padding for weakly supervised saliency prediction in 360 deg videos"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "end to end saliency mapping via probability distribution prediction"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "viraliency pooling local virality"
    - "the 2017 davis challenge on video object segmentation"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "semantic driven generation of hyperlapse from 360 circ video"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-5 (induced by source paper "a memory network approach for story based temporal summarization of 360 deg videos"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "a read write memory network for movie story understanding"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "learning visual storylines with skipping recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "video summarization with long short term memory"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning of depth and camera motion from 360 deg videos"):
  with members:
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "3d visual perception for self driving cars using a multi camera system calibration mapping localization and obstacle detection"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"

Connected through hyperedge HE-7 (induced by source paper "snap angle prediction for 360 circ panorama"):
  with members:
    - "pixel objectness learning to segment generic objects automatically in images and videos"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "action search spotting actions in videos and its application to temporal action localization"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "photo aesthetics ranking network with attributes and content adaptation"

Connected through hyperedge HE-8 (induced by source paper "a deep ranking model for spatio temporal highlight detection from a 360 video"):
  with members:
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "video2gif automatic generation of animated gifs from video"
    - "r fcn object detection via region based fully convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"

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)