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: "fast back projection for non line of sight reconstruction"
  Abstract: Recent works have demonstrated non-line of sight (NLOS) reconstruction by using the time-resolved signal from multiply scattered light. These works combine ultrafast imaging systems with computation, which back-projects the recorded space-time signal to build a probabilistic map of the hidden geometry. Unfortunately, this computation is slow, becoming a bottleneck as the imaging technology improves. In this work, we propose a new back-projection technique for NLOS reconstruction, which is up to a thousand times faster than previous work, with almost no quality loss. We base on the observation that the hidden geometry probability map can be built as the intersection of the three-bounce space-time manifolds defined by the light illuminating the hidden geometry and the visible point receiving the scattered light from such hidden geometry. This allows us to pose the reconstruction of the hidden geometry as the voxelization of these space-time manifolds, which has lower theoretic complexity and is easily implementable in the GPU. We demonstrate the efficiency and quality of our technique compared against previous methods in both captured and synthetic data.

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"
    - "convolutional pose machines"
    - "learning a predictable and generative vector representation for objects"
    - "a large dataset of object scans"

Connected through hyperedge HE-2 (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"
    - "optically lightweight tracking of objects around a corner"
    - "adam a method for stochastic optimization"

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"
    - "robust non line of sight imaging with single photon detectors"
    - "recent advances in transient imaging a computer graphics and vision perspective"
    - "optically lightweight tracking of objects around a corner"

Connected through hyperedge HE-4 (induced by source paper "progressive transient photon beams"):
  with members:
    - "recent advances in transient imaging a computer graphics and vision perspective"
    - "optically lightweight tracking of objects around a corner"

Connected through hyperedge HE-5 (induced by source paper "virtual wave optics for non line of sight imaging"):
  with members:
    - "recent advances in transient imaging a computer graphics and vision perspective"
    - "spatial phase sweep increasing temporal resolution of transient imaging using a light source array"

Connected through hyperedge HE-6 (induced by source paper "non line of sight imaging with partial occluders and surface normals"):
  with members:
    - "non line of sight tracking of people at long range"
    - "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)