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 "on detection of faint edges in noisy images"):
  Abstract: A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected. In this paper we offer a formalism to study this question and subsequently introduce computationally efficient multiscale edge detection algorithms designed to detect faint edges in noisy images. In our formalism we view edge detection as a search in a discrete, though potentially large, set of feasible curves. First, we derive approximate expressions for the detection threshold as a function of curve length and the complexity of the search space. We then present two edge detection algorithms, one for straight edges, and the second for curved ones. Both algorithms efficiently search for edges in a large set of candidates by hierarchically constructing difference filters that match the curves traced by the sought edges. We demonstrate the utility of our algorithms in both simulations and applications involving challenging real images. Finally, based on these principles, we develop an algorithm for fiber detection and enhancement. We exemplify its utility to reveal and enhance nerve axons in light microscopy images.

Includes members:
  Member "richer convolutional features for edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "multi stage multi recursive input fully convolutional networks for neuronal boundary detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "boundary aware feature propagation for scene segmentation")
  Member "convolutional oriented boundaries from image segmentation to high level tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "convolutional oriented boundaries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "instance segmentation with point supervision")
  Member "object contour detection with a fully convolutional encoder decoder network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "fast detection of curved edges at low snr":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep faster detection of faint edges in noisy images")
  Member "holistically nested edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "non local euclidean medians":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pruned non local means")

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)