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: "regression on fixed rank positive semidefinite matrices a riemannian approach"
  Abstract: The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixed-rank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks.

Connected through hyperedge HE-1 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "deep covariance descriptors for facial expression recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "automatic analysis of facial expressions based on deep covariance trajectories"

Connected through hyperedge HE-2 (induced by source paper "adversarial metric learning"):
  with members:
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "seven deep semi supervised verification networks"
    - "learning to compare image patches via convolutional neural networks"
    - "deep metric learning via lifted structured feature embedding"
    - "robust metric learning by smooth optimization"

Connected through hyperedge HE-3 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"

Connected through hyperedge HE-4 (induced by source paper "geometry aware similarity learning on spd manifolds for visual recognition"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian network for spd matrix learning"
    - "supervised logeuclidean metric learning for symmetric positive definite matrices"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "bregman divergences for infinite dimensional covariance matrices"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"

Connected through hyperedge HE-5 (induced by source paper "manopt a matlab toolbox for optimization on manifolds"):
  with members:
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-6 (induced by source paper "fixed rank matrix factorizations and riemannian low rank optimization"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a riemannian geometry for low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "subspace evolution and transfer set for low rank matrix completion"
    - "a geometric approach to low rank matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

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.LG(Machine Learning)