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 "conservative wasserstein training for pose estimation"):
  Abstract: This paper targets the task with discrete and periodic class labels ($e.g.,$ pose/orientation estimation) in the context of deep learning. The commonly used cross-entropy or regression loss is not well matched to this problem as they ignore the periodic nature of the labels and the class similarity, or assume labels are continuous value. We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i.e.,$ using arc length of a circle) or adaptively learning the ground metric. We extend the ground metric as a linear, convex or concave increasing function $w.r.t.$ arc length from an optimization perspective. We also propose to construct the conservative target labels which model the inlier and outlier noises using a wrapped unimodal-uniform mixture distribution. Unlike the one-hot setting, the conservative label makes the computation of Wasserstein distance more challenging. We systematically conclude the practical closed-form solution of Wasserstein distance for pose data with either one-hot or conservative target label. We evaluate our method on head, body, vehicle and 3D object pose benchmarks with exhaustive ablation studies. The Wasserstein loss obtaining superior performance over the current methods, especially using convex mapping function for ground metric, conservative label, and closed-form solution.

Includes members:
  Member "unsupervised domain adaptation via calibrating uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation")
  Member "dependency aware attention control for unconstrained face recognition with image sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "deep verifier networks verification of deep discriminative models with deep generative models":
    also connected through other hyperedges:
      (none)
  Member "confidence regularized self training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels")
  Member "3d pose estimation and 3d model retrieval for objects in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss")
  Member "a mixed classification regression framework for 3d pose estimation from 2d images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "starmap for category agnostic keypoint and viewpoint estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "objects as points")
  Member "deep directional statistics pose estimation with uncertainty quantification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mixture dense regression for object detection and human pose estimation")

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)