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: "multi action recognition via stochastic modelling of optical flow and gradients"
  Abstract: In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Connected through hyperedge HE-1 (induced by source paper "comparative evaluation of action recognition methods via riemannian manifolds fisher vectors and gmms ideal and challenging conditions"):
  with members:
    - "joint recognition and segmentation of actions via probabilistic integration of spatio temporal fisher vectors"
    - "bags of affine subspaces for robust object tracking"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "learning discriminative stein kernel for spd matrices and its applications"
    - "log euclidean bag of words for human action recognition"
    - "random projections on manifolds of symmetric positive definite matrices for image classification"
    - "optimizing over radial kernels on compact manifolds"
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"

Connected through hyperedge HE-2 (induced by source paper "towards miss universe automatic prediction the evening gown competition"):
  with members:
    - "p cnn pose based cnn features for action recognition"
    - "fine grained activity recognition with holistic and pose based features"

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)