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 "domain specific face synthesis for video face recognition from a single sample per person"):
  Abstract: In video surveillance, face recognition (FR) systems are employed to detect individuals of interest appearing over a distributed network of cameras. The performance of still-to-video FR systems can decline significantly because faces captured in unconstrained operational domain (OD) over multiple video cameras have a different underlying data distribution compared to faces captured under controlled conditions in the enrollment domain with a still camera. This is particularly true when individuals are enrolled to the system using a single reference still. To improve the robustness of these systems, it is possible to augment the reference set by generating synthetic faces based on the original still. However, without the knowledge of the OD, many synthetic images must be generated to account for all possible capture conditions. FR systems may, therefore, require complex implementations and yield lower accuracy when training on many less relevant images. This paper introduces an algorithm for domain-specific face synthesis (DSFS) that exploits the representative intra-class variation information available from the OD. Prior to operation (during camera calibration), a compact set of faces from unknown persons appearing in the OD is selected through affinity propagation clustering in the captured condition space (defined by pose and illumination estimation). The domain-specific variations of these face images are then projected onto the reference still of each individual by integrating an image-based face relighting technique inside the 3-D reconstruction framework. A compact set of synthetic faces is generated that resemble individuals of interest under the capture conditions relevant to the OD. In a particular implementation based on sparse representation classification, the synthetic faces generated with the DSFS are employed to form a cross-domain dictionary that accounts for structured sparsity, where the dictionary blocks combine the original and synthetic faces of each individual. Experimental results obtained with videos from the Chokepoint and COX-S2V data sets reveal that augmenting the reference gallery set of still-to-video FR systems using the proposed DSFS approach can provide a significantly higher level of accuracy compared with the state-of-the-art approaches, with only a moderate increase in its computational complexity.

Includes members:
  Member "semi supervised sparse representation based classification for face recognition with insufficient labeled samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymmetric rejection loss for fairer face recognition")
  Member "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "do we really need to collect millions of faces for effective face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing face representation with unlabeled data")
  Member "learning from simulated and unsupervised images through adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "face recognition using deep multi pose representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for face recognition a critical analysis")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "regressing robust and discriminative 3d morphable models with a very deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "face hallucination with finishing touches")
  Member "learning detailed face reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the whole is greater than the sum of its nonrigid parts")

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)