human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep learning can reverse photon migration for diffuse optical tomography"):
  Abstract: Can artificial intelligence (AI) learn complicated non-linear physics? Here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains accurate 3D distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches to inverse problems, our deep network learns to invert the Lippmann-Schwinger integral equation which describes the essential physics of photon migration of diffuse near-infrared (NIR) photons in turbid media. As an example for clinical relevance, we applied the method to our prototype diffuse optical tomography (DOT). We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.

Includes members:
  Member "image reconstruction by domain transform manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound")
  Member "deep learning for photoacoustic tomography from sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully dense unet for 2 d sparse photoacoustic tomography artifact removal")
  Member "deep convolutional neural network for inverse problems in imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)