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 "deep sketch photo face recognition assisted by facial attributes"):
  Abstract: In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a cou- pled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with differ- ent facial attributes, while the verification task reduces the intra-personal variations by pulling together all the fea- tures that are related to one person. The learned discrim- inative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary fa- cial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Exten- sive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state- of-the-art models in sketch-photo recognition algorithms

Includes members:
  Member "attribute centered loss for soft biometrics guided face sketch photo recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a comprehensive overview of biometric fusion")
  Member "multi level feature abstraction from convolutional neural networks for multimodal biometric identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending against adversarial iris examples using wavelet decomposition")
  Member "fingerprint distortion rectification using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "white box evaluation of fingerprint matchers robustness to minutiae perturbations")
  Member "deep cross polarimetric thermal to visible face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polarimetric thermal to visible face verification via self attention guided synthesis")
  Member "generalized bilinear deep convolutional neural networks for multimodal biometric identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "defending against adversarial iris examples using wavelet decomposition")
  Member "3d convolutional neural networks for cross audio visual matching recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals")
  Member "face attribute prediction using off the shelf cnn features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attribute guided coupled gan for cross resolution face recognition")
  Member "sparse graphical representation based discriminant analysis for heterogeneous face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relational deep feature learning for heterogeneous face 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)