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: "robust non line of sight imaging with single photon detectors"
  Abstract: Imaging objects that are obscured by scattering and occlusion is an important challenge for many applications. For example, navigation and mapping capabilities of autonomous vehicles could be improved, vision in harsh weather conditions or under water could be facilitated, or search and rescue scenarios could become more effective. Unfortunately, conventional cameras cannot see around corners. Emerging, time-resolved computational imaging systems, however, have demonstrated first steps towards non-line-of-sight (NLOS) imaging. In this paper, we develop an algorithmic framework for NLOS imaging that is robust to partial occlusions within the hidden scenes. This is a common light transport effect, but not adequately handled by existing NLOS reconstruction algorithms, resulting in fundamental limitations in what types of scenes can be recovered. We demonstrate state-of-the-art NLOS reconstructions in simulation and with a prototype single photon avalanche diode (SPAD) based acquisition system.

Connected through hyperedge HE-1 (induced by source paper "a bit too much high speed imaging from sparse photon counts"):
  with members:
    - "video enhancement with task oriented flow"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deep residual learning for image recognition"
    - "llnet a deep autoencoder approach to natural low light image enhancement"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-2 (induced by source paper "steady state non line of sight imaging"):
  with members:
    - "non line of sight imaging with partial occluders and surface normals"
    - "non line of sight tracking of people at long range"
    - "neural network identification of people hidden from view with a single pixel single photon detector"
    - "unsupervised monocular depth estimation with left right consistency"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "optically lightweight tracking of objects around a corner"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "non line of sight reconstruction using efficient transient rendering"):
  with members:
    - "non line of sight imaging with partial occluders and surface normals"
    - "fast back projection for non line of sight reconstruction"
    - "recent advances in transient imaging a computer graphics and vision perspective"
    - "optically lightweight tracking of objects around a corner"

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)