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: "the next best underwater view"
  Abstract: To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in a participating medium. Moreover, underwater and in other media, artificial lighting is needed. Overall, data quality depends on the observed surface, medium and the time-varying poses of the camera and light source. This work proposes to optimize camera/light poses as they move, so that the surface is scanned efficiently and the descattered recovery has the highest quality. The work generalizes the next best view concept of robot vision to scattering media and cooperative movable lighting. It also extends descattering to platforms that move optimally. The optimization criterion is information gain, taken from information theory. We exploit the existence of a prior rough 3D model, since underwater such a model is routinely obtained using sonar. We demonstrate this principle in a scaled-down setup.

Connected through hyperedge HE-1 (induced by source paper "an underwater image enhancement benchmark dataset and beyond"):
  with members:
    - "diving deeper into underwater image enhancement a survey"
    - "salient object detection in the deep learning era an in depth survey"
    - "nested network with two stream pyramid for salient object detection in optical remote sensing images"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "emerging from water underwater image color correction based on weakly supervised color transfer"
    - "underwater single image color restoration using haze lines and a new quantitative dataset"
    - "an iterative co saliency framework for rgbd images"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "effects of image degradations to cnn based image classification"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "real time detection tracking and classification of moving and stationary objects using multiple fisheye images"
    - "squeeze and excitation networks"
    - "dehazenet an end to end system for single image haze removal"
    - "how image degradations affect deep cnn based face recognition"
    - "parametric object motion from blur"
    - "studying very low resolution recognition using deep networks"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "underwater single image color restoration using haze lines and a new quantitative dataset"):
  with members:
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-4 (induced by source paper "a reinforcement learning approach to the view planning problem"):
  with members:
    - "uav based autonomous image acquisition with multi view stereo quality assurance by confidence prediction"
    - "recurrent attention models for depth based person identification"
    - "recurrent models of visual attention"
    - "analytical approach to parallel repetition"
    - "playing atari with deep reinforcement learning"

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)