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: "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
  Abstract: Images captured by fisheye lenses violate the pinhole camera assumption and suffer from distortions. Rectification of fisheye images is therefore a crucial preprocessing step for many computer vision applications. In this paper, we propose an end-to-end multi-context collaborative deep network for removing distortions from single fisheye images. In contrast to conventional approaches, which focus on extracting hand-crafted features from input images, our method learns high-level semantics and low-level appearance features simultaneously to estimate the distortion parameters. To facilitate training, we construct a synthesized dataset that covers various scenes and distortion parameter settings. Experiments on both synthesized and real-world datasets show that the proposed model significantly outperforms current state of the art methods. Our code and synthesized dataset will be made publicly available.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "degeneracy in self calibration revisited and a deep learning solution for uncalibrated slam"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "baseline desensitizing in translation averaging"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "rolling shutter aware differential sfm and image rectification"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "deep supervision with intermediate concepts"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-3 (induced by source paper "single view distortion correction using semantic guidance"):
  with members:
    - "carla an open urban driving simulator"
    - "deep image harmonization"
    - "optical flow with semantic segmentation and localized layers"
    - "robust scene text recognition with automatic rectification"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "learning to calibrate straight lines for fisheye image rectification"):
  with members:
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "richer convolutional features for edge detection"
    - "semantic scene completion from a single depth image"
    - "stacked hourglass networks for human pose estimation"
    - "holistically nested edge detection"
    - "pixel wise deep learning for contour detection"
    - "deep residual learning for image recognition"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"

Connected through hyperedge HE-5 (induced by source paper "amalgamating filtered knowledge learning task customized student from multi task teachers"):
  with members:
    - "amalgamating knowledge towards comprehensive classification"
    - "deep label distribution learning with label ambiguity"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "hcp a flexible cnn framework for multi label image classification"
    - "cross stitch networks for multi task learning"
    - "identity mappings in deep residual networks"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-6 (induced by source paper "a light dual task neural network for haze removal"):
  with members:
    - "gated fusion network for single image dehazing"
    - "improving object detection from scratch via gated feature reuse"
    - "deep voice real time neural text to speech"
    - "an all in one network for dehazing and beyond"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "dehazenet an end to end system for single image haze removal"
    - "predicting people s 3d poses from short sequences"
    - "what players do with the ball a physically constrained interaction modeling"

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)