human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "confidence regularized self training"):
  with members:
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "open set domain adaptation for image and action recognition"
    - "unsupervised visual domain adaptation a deep max margin gaussian process approach"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-2 (induced by source paper "attention control with metric learning alignment for image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "revisiting temporal modeling for video based person reid"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "learning policies for adaptive tracking with deep feature cascades"

Connected through hyperedge HE-3 (induced by source paper "permutation invariant feature restructuring for correlation aware image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "deep sets"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "interpretable set functions"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"
    - "deep reinforcement learning that matters"

Question: Please tell me which class the center node 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)