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: "towards a generative approach to activity recognition and segmentation"
  Abstract: As research on action recognition matures, the focus is gradually shifting away from categorizing manually-segmented clips into basic action units to parsing and understanding long action sequences that make up human daily activities. There is a long history of applying structured models for the analysis of temporal sequences. Yet, while they seem like an obvious choice for the recognition of human activities, they have not quite reached a level of maturity in vision which is comparable to that speech recognition. With the widespread availability of large video datasets, combined with recent progress in the development of compact feature representations, the time seems ripe to revisit structured generative approaches. We propose an end-to-end generative framework which uses reduced Fisher Vectors (FVs) in conjunction with structured temporal models for the segmentation and recognition of video sequences. It shows that the overall generative properties of FVs make them especially suitable for a combination with generative models like Gaussian Mixtures. The proposed approach is extensively evaluated on a variety of action recognition datasets ranging from human cooking activities to animal behavioral analysis. It shows that the architecture, despite its simplicity, it is able to outperform complex state-of-the-art approaches on all larger datasets.

Connected through hyperedge HE-1 (induced by source paper "sembed semantic embedding of egocentric action videos"):
  with members:
    - "going deeper into first person activity recognition"
    - "unsupervised learning from narrated instruction videos"
    - "pooled motion features for first person videos"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)