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: "neural network identification of people hidden from view with a single pixel single photon detector"
  Abstract: Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with an artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N=3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times

Connected through hyperedge HE-1 (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"
    - "optically lightweight tracking of objects around a corner"
    - "multi view 3d object detection network for autonomous driving"
    - "you only look once unified real time object detection"

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"
    - "robust non line of sight imaging with single photon detectors"
    - "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"

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)