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 "learning with known operators reduces maximum training error bounds"):
  Abstract: We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.

Includes members:
  Member "pyro nn python reconstruction operators in neural networks":
    also connected through other hyperedges:
      (none)
  Member "visual object networks image generation with disentangled 3d representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "st gan spatial transformer generative adversarial networks for image compositing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "user loss a forced choice inspired approach to train neural networks directly by user interaction":
    also connected through other hyperedges:
      (none)
  Member "deriving neural network architectures using precision learning parallel to fan beam conversion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a gentle introduction to deep learning in medical image processing")
  Member "distribution matching losses can hallucinate features in medical image translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visualisation of medical image fusion and translation for accurate diagnosis of high grade gliomas")
  Member "a gentle introduction to deep learning in medical image processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis")
  Member "fast end to end trainable guided filter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")

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.LG(Machine Learning)