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: "optically lightweight tracking of objects around a corner"
  Abstract: The observation of objects located in inaccessible regions is a recurring challenge in a wide variety of important applications. Recent work has shown that using rare and expensive optical setups, indirect diffuse light reflections can be used to reconstruct objects and two-dimensional (2D) patterns around a corner. Here we show that occluded objects can be tracked in real time using much simpler means, namely a standard 2D camera and a laser pointer. Our method fundamentally differs from previous solutions by approaching the problem in an analysis-by-synthesis sense. By repeatedly simulating light transport through the scene, we determine the set of object parameters that most closely fits the measured intensity distribution. We experimentally demonstrate that this approach is capable of following the translation of unknown objects, and translation and orientation of a known object, in real time.

Connected through hyperedge HE-1 (induced by source paper "deep non line of sight reconstruction"):
  with members:
    - "non line of sight reconstruction using efficient transient rendering"
    - "occupancy networks learning 3d reconstruction in function space"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "non line of sight imaging with partial occluders and surface normals"
    - "learning representations and generative models for 3d point clouds"
    - "fast back projection for non line of sight reconstruction"
    - "convolutional pose machines"
    - "learning a predictable and generative vector representation for objects"

Connected through hyperedge HE-2 (induced by source paper "seeing around street corners non line of sight detection and tracking in the wild using doppler radar"):
  with members:
    - "pointpillars fast encoders for object detection from point clouds"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "non line of sight tracking of people at long range"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "neural network identification of people hidden from view with a single pixel single photon detector"
    - "multi view 3d object detection network for autonomous driving"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "computational mirrors blind inverse light transport by deep matrix factorization"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "neural blind deconvolution using deep priors"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "blind image deconvolution using deep generative priors"
    - "deep image prior"
    - "a deep matrix factorization method for learning attribute representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "keyhole imaging non line of sight imaging and tracking of moving objects along a single optical path at long standoff distances"):
  with members:
    - "the hasyv2 dataset"
    - "fast back projection for non line of sight reconstruction"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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"
    - "robust non line of sight imaging with single photon detectors"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (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"
    - "robust non line of sight imaging with single photon detectors"
    - "fast back projection for non line of sight reconstruction"
    - "recent advances in transient imaging a computer graphics and vision perspective"

Connected through hyperedge HE-7 (induced by source paper "progressive transient photon beams"):
  with members:
    - "fast back projection for non line of sight reconstruction"
    - "recent advances in transient imaging a computer graphics and vision perspective"

Connected through hyperedge HE-8 (induced by source paper "multi modal non line of sight passive imaging"):
  with members:
    - "joint defogging and demosaicking"

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)