human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a dual fast and slow feature interaction in biologically inspired visual recognition of human action"):
  Abstract: Computational neuroscience studies that have examined human visual system through functional magnetic resonance imaging (fMRI) have identified a model where the mammalian brain pursues two distinct pathways (for recognition of biological movement tasks). In the brain, dorsal stream analyzes the information of motion (optical flow), which is the fast features, and ventral stream (form pathway) analyzes form information (through active basis model based incremental slow feature analysis ) as slow features. The proposed approach suggests the motion perception of the human visual system composes of fast and slow feature interactions that identifies biological movements. Form features in the visual system biologically follows the application of active basis model with incremental slow feature analysis for the extraction of the slowest form features of human objects movements in the ventral stream. Applying incremental slow feature analysis provides an opportunity to use the action prototypes. To extract the slowest features episodic observation is required but the fast features updates the processing of motion information in every frames. Experimental results have shown promising accuracy for the proposed model and good performance with two datasets (KTH and Weizmann).

Includes members:
  Member "slow feature analysis for human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised deep slow feature analysis for change detection in multi temporal remote sensing images")
  Member "bio inspired human action recognition using hybrid max product neuro fuzzy classifier and quantum behaved pso":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)