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 "end to end learned random walker for seeded image segmentation"):
  Abstract: We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the underlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. Besides calculating the exact gradient for optimizing these diffusivities, we also propose simplifications that sparsely sample the gradient and still yield competitive results. The proposed method achieves the currently best results on a seeded version of the CREMI neuron segmentation challenge.

Includes members:
  Member "learned watershed end to end learning of seeded segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "patchperpix for instance segmentation")
  Member "superhuman accuracy on the snemi3d connectomics challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non local u net for biomedical image segmentation")
  Member "learning random walk label propagation for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "optnet differentiable optimization as a layer in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "convolutional oriented boundaries from image segmentation to high level tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "convolutional random walk networks for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constrained dominant sets and its applications in computer vision")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")

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)