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: "local ternary patterns from three orthogonal planes for human action classification"
  Abstract: Research highlights? LBP features extracted from the human silhouette. ? Random subspace for human action classification. ? To combine LBP-TOP with LTP. Human action classification is a new field of study with applications ranging from automatically labeling video segments to recognition of suspicious behavior in video surveillance cameras. In this paper we present some variants of Local Binary Patterns from Three Orthogonal Planes (LBP-TOP), which is considered one of the state of the art texture descriptors for human action classification. The standard LBP-TOP operator is defined as a gray-scale invariant texture measure, derived from the standard Local Binary Patterns (LBP). It is obtained by calculating the LBP features from the xt and yt planes of a space-time volume. Our LBP-TOP variants combine the idea of LBP-TOP with Local Ternary Patterns (LTP). The encoding of LTP is used for the evaluation of the local gray-scale difference in the different planes of the space-time volume. Different histograms are concatenated to form the feature vector and a random subspace of linear support vector machines is used for classifying action using the Weizmann database of video images. To the best of our knowledge, our method offers the first set of classification experiments to obtain 100% accuracy using the 10-class Weizmann dataset.

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)