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 "deformation aware unpaired image translation for pose estimation on laboratory animals"):
  Abstract: Our goal is to capture the pose of neuroscience model organisms, without using any manual supervision, to be able to study how neural circuits orchestrate behaviour. Human pose estimation attains remarkable accuracy when trained on real or simulated datasets consisting of millions of frames. However, for many applications simulated models are unrealistic and real training datasets with comprehensive annotations do not exist. We address this problem with a new sim2real domain transfer method. Our key contribution is the explicit and independent modeling of appearance, shape and poses in an unpaired image translation framework. Our model lets us train a pose estimator on the target domain by transferring readily available body keypoint locations from the source domain to generated target images. We compare our approach with existing domain transfer methods and demonstrate improved pose estimation accuracy on Drosophila melanogaster (fruit fly), Caenorhabditis elegans (worm) and Danio rerio (zebrafish), without requiring any manual annotation on the target domain and despite using simplistic off-the-shelf animal characters for simulation, or simple geometric shapes as models. Our new datasets, code, and trained models will be published to support future neuroscientific studies.

Includes members:
  Member "amass archive of motion capture as surface shapes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "movi a large multipurpose motion and video dataset")
  Member "transgaga geometry aware unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "emergence of object segmentation in perturbed generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "three d safari learning to estimate zebra pose shape and texture from images in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "learning conditional deformable templates with convolutional networks":
    also connected through other hyperedges:
      (none)
  Member "understanding straight through estimator in training activation quantized neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations")
  Member "voxelmorph a learning framework for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lt net label transfer by learning reversible voxel wise correspondence for one shot medical image segmentation")

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)