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 "feature level fusion from facial attributes for face recognition"):
  Abstract: We introduce a deep convolutional neural networks (CNN) architecture to classify facial attributes and recognize face images simultaneously via a shared learning paradigm to improve the accuracy for facial attribute prediction and face recognition performance. In this method, we use facial attributes as an auxiliary source of information to assist CNN features extracted from the face images to improve the face recognition performance. Specifically, we use a shared CNN architecture that jointly predicts facial attributes and recognize face images simultaneously via a shared learning parameters, and then we use facial attribute features an an auxiliary source of information concatenated by face features to increase the discrimination of the CNN for face recognition. This process assists the CNN classifier to better recognize face images. The experimental results show that our model increases both the face recognition and facial attribute prediction performance, especially for the identity attributes such as gender and race. We evaluated our method on several standard datasets labeled by identities and face attributes and the results show that the proposed method outperforms state-of-the-art face recognition models.

Includes members:
  Member "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "attribute guided coupled gan for cross resolution face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "label propagation for deep semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification")
  Member "unsupervised facial geometry learning for sketch to photo synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "transitive hashing network for heterogeneous multimedia retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance effectiveness of multimedia information search using the epsilon greedy algorithm")
  Member "face attribute prediction using off the shelf cnn features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attribute guided coupled gan for cross resolution face recognition")
  Member "restoring highly corrupted images by impulse noise using radial basis functions interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep sparse band selection for hyperspectral face recognition")
  Member "targeting ultimate accuracy face recognition via deep embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry")

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)