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 "multi fold gabor pca and ica filter convolution descriptor for face recognition"):
  Abstract: This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (   $\mathcal {M}$   -FFC), for face recognition. On the assumption that    $\mathcal {M}$   -FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by    $\mathcal {M}$   -fold to instantiate a filter offspring set. The    $\mathcal {M}$   -FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA, and ICA filters thus yields three offspring sets: 1) Gabor filters solely; 2) Gabor-PCA filters; and 3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for    $\mathcal {M}$   -FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.

Includes members:
  Member "multi directional multi level dual cross patterns for robust face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ldop local directional order pattern for robust face retrieval")
  Member "deepid3 face recognition with very deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "palmprint recognition in uncontrolled and uncooperative environment")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a comprehensive survey on pose invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "dctnet a simple learning free approach for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "momentsnet a simple learning free method for binary image recognition")
  Member "pcanet a simple deep learning baseline for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "effective face frontalization in unconstrained images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")

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)