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 trainable active contours via differentiable rendering"):
  Abstract: We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the polygon shape and the ground truth segmentation mask. The network employs a neural renderer to create the polygon from its vertices, making the process fully differentiable. We demonstrate that our method outperforms the state of the art segmentation networks and deep active contour solutions in a variety of benchmarks, including medical imaging and aerial images. Our code is available at this https URL.

Includes members:
  Member "cnn based semantic segmentation using level set loss":
    also connected through other hyperedges:
      (none)
  Member "darnet deep active ray network for building segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polytransform deep polygon transformer for instance segmentation")
  Member "mesh r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "fast interactive object annotation with curve gcn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep snake for real time instance segmentation")
  Member "efficient interactive annotation of segmentation datasets with polygon rnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "improved breast mass segmentation in mammograms with conditional residual u net":
    also connected through other hyperedges:
      (none)
  Member "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "unsupervised learning of shape and pose with differentiable point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")

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)