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: "pano2vid automatic cinematography for watching 360 circ videos"
  Abstract: We introduce the novel task of Pano2Vid $-$ automatic cinematography in panoramic 360$^{\circ}$ videos. Given a 360$^{\circ}$ video, the goal is to direct an imaginary camera to virtually capture natural-looking normal field-of-view (NFOV) video. By selecting "where to look" within the panorama at each time step, Pano2Vid aims to free both the videographer and the end viewer from the task of determining what to watch. Towards this goal, we first compile a dataset of 360$^{\circ}$ videos downloaded from the web, together with human-edited NFOV camera trajectories to facilitate evaluation. Next, we propose AutoCam, a data-driven approach to solve the Pano2Vid task. AutoCam leverages NFOV web video to discriminatively identify space-time "glimpses" of interest at each time instant, and then uses dynamic programming to select optimal human-like camera trajectories. Through experimental evaluation on multiple newly defined Pano2Vid performance measures against several baselines, we show that our method successfully produces informative videos that could conceivably have been captured by human videographers.

Connected through hyperedge HE-1 (induced by source paper "visual question answering on 360 deg images"):
  with members:
    - "towards vqa models that can read"
    - "uniter learning universal image text representations"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "vl bert pre training of generic visual linguistic representations"
    - "360 indoor towards learning real world objects in 360 deg indoor equirectangular images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "learning to localize sound sources in visual scenes analysis and applications"):
  with members:
    - "audio visual model distillation using acoustic images"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"
    - "2 5d visual sound"

Connected through hyperedge HE-3 (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"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "self view grounding given a narrated 360 deg video"

Connected through hyperedge HE-4 (induced by source paper "distortion adaptive salient object detection in 360 circ omnidirectional images"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "deeply supervised salient object detection with short connections"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-5 (induced by source paper "spatial and temporal consistency aware dynamic adaptive streaming for 360 degree videos"):
  with members:
    - "adaptive streaming in interactive multiview video systems"
    - "end to end saliency mapping via probability distribution prediction"
    - "viewport adaptation based immersive video streaming perceptual modeling and applications"
    - "multiple object recognition with visual attention"

Connected through hyperedge HE-6 (induced by source paper "eliminating the blind spot adapting 3d object detection and monocular depth estimation to 360 deg panoramic imagery"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "cycada cycle consistent adversarial domain adaptation"
    - "carla an open urban driving simulator"
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "adversarial discriminative domain adaptation"
    - "learning spherical convolution for fast features from 360 deg imagery"

Connected through hyperedge HE-7 (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-8 (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"

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)