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 "ensemble of hankel matrices for face emotion recognition"):
  Abstract: In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These concurrent signals are represented by means of a set of multi-scale appearance features that might be correlated with one or more concurrent signals. The extraction of these appearance features from a sequence of face images yields to a set of time series. This paper proposes to use the dynamics regulating each appearance feature time series to recognize among different face emotions. To this purpose, an ensemble of Hankel matrices corresponding to the extracted time series is used for emotion classification within a framework that combines nearest neighbor and a majority vote schema. Experimental results on a public available dataset shows that the adopted representation is promising and yields state-of-the-art accuracy in emotion classification.

Includes members:
  Member "using hankel matrices for dynamics based facial emotion recognition and pain detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep video gesture recognition using illumination invariants")
  Member "a generative restricted boltzmann machine based method for high dimensional motion data modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational conditional dependence hidden markov models for human action recognition")
  Member "emotional expression classification using time series kernels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "continuous emotion recognition via deep convolutional autoencoder and support vector regressor")
  Member "compressive acquisition of dynamic scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy preserving action recognition using coded aperture videos")
  Member "a kernel for time series based on global alignments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition")

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)