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: "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
  Abstract: Watching a 360{\deg} sports video requires a viewer to continuously select a viewing angle, either through a sequence of mouse clicks or head movements. To relieve the viewer from this "360 piloting" task, we propose "deep 360 pilot" -- a deep learning-based agent for piloting through 360{\deg} sports videos automatically. At each frame, the agent observes a panoramic image and has the knowledge of previously selected viewing angles. The task of the agent is to shift the current viewing angle (i.e. action) to the next preferred one (i.e., goal). We propose to directly learn an online policy of the agent from data. We use the policy gradient technique to jointly train our pipeline: by minimizing (1) a regression loss measuring the distance between the selected and ground truth viewing angles, (2) a smoothness loss encouraging smooth transition in viewing angle, and (3) maximizing an expected reward of focusing on a foreground object. To evaluate our method, we build a new 360-Sports video dataset consisting of five sports domains. We train domain-specific agents and achieve the best performance on viewing angle selection accuracy and transition smoothness compared to [51] and other baselines.

Connected through hyperedge HE-1 (induced by source paper "360 o surface regression with a hyper sphere loss"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "three for one and one for three flow segmentation and surface normals"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-2 (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"
    - "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"
    - "mask r cnn"

Connected through hyperedge HE-3 (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"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"

Connected through hyperedge HE-4 (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"
    - "salnet360 saliency maps for omni directional images with cnn"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "a deep multi level network for saliency prediction"

Connected through hyperedge HE-5 (induced by source paper "one shot imitation filming of human motion videos"):
  with members:
    - "yolov3 an incremental improvement"
    - "directing cinematographic drones"
    - "one shot imitation learning"
    - "automated cinematography with unmanned aerial vehicles"
    - "temporal segment networks towards good practices for deep action recognition"
    - "towards a drone cinematographer guiding quadrotor cameras using visual composition principles"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

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"
    - "learning so 3 equivariant representations with spherical cnns"

Connected through hyperedge HE-8 (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"
    - "a read write memory network for movie story understanding"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "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"

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)