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: "riemannian dictionary learning and sparse coding for positive definite matrices"
  Abstract: Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas of computer vision and machine learning. While these matrices form an open subset of the Euclidean space of symmetric matrices, viewing them through the lens of non-Euclidean Riemannian geometry often turns out to be better suited in capturing several desirable data properties. However, formulating classical machine learning algorithms within such a geometry is often non-trivial and computationally expensive. Inspired by the great success of dictionary learning and sparse coding for vector-valued data, our goal in this paper is to represent data in the form of SPD matrices as sparse conic combinations of SPD atoms from a learned dictionary via a Riemannian geometric approach. To that end, we formulate a novel Riemannian optimization objective for dictionary learning and sparse coding in which the representation loss is characterized via the affine invariant Riemannian metric. We also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets demonstrate superior classification and retrieval performance using our approach when compared to sparse coding via alternative non-Riemannian formulations.

Connected through hyperedge HE-1 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "a deep learning perspective on the origin of facial expressions"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spontaneous subtle expression detection and recognition based on facial strain"

Connected through hyperedge HE-2 (induced by source paper "riemannian adaptive optimization methods"):
  with members:
    - "on the convergence of adam and beyond"
    - "hyperbolic recommender systems"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "riemannian approach to batch normalization"

Connected through hyperedge HE-3 (induced by source paper "toward dual functional radar communication systems optimal waveform design"):
  with members:
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "joint design of overlaid communication systems and pulsed radars"
    - "mu mimo communications with mimo radar from co existence to joint transmission"
    - "robust mimo beamforming for cellular and radar coexistence"
    - "interference exploitation for radar and cellular coexistence the power efficient approach"
    - "an efficient global algorithm for single group multicast beamforming"
    - "an efficient manifold algorithm for constructive interference based constant envelope precoding"
    - "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system"

Connected through hyperedge HE-4 (induced by source paper "riemannian kernel based nystrom method for approximate infinite dimensional covariance descriptors with application to image set classification"):
  with members:
    - "multi focus image fusion using dictionary learning and low rank representation"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"

Connected through hyperedge HE-5 (induced by source paper "component spd matrices a low dimensional discriminative data descriptor for image set classification"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"

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)