human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "self supervised learning of face representations for video face clustering"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "sspp dan deep domain adaptation network for face recognition with single sample per person"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-2 (induced by source paper "cross domain face synthesis using a controllable gan"):
  with members:
    - "representation learning by rotating your faces"
    - "conditional image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "robust conditional generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "toward multimodal image to image translation"
    - "associative domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "a paired sparse representation model for robust face recognition from a single sample"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "sphereface deep hypersphere embedding for face recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "effective face frontalization in unconstrained images"

Connected through hyperedge HE-4 (induced by source paper "generating photo realistic training data to improve face recognition accuracy"):
  with members:
    - "representation learning by rotating your faces"
    - "training deep face recognition systems with synthetic data"
    - "privacy protective gan for face de identification"
    - "recent progress of face image synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"

Question: Please tell me which class the center node 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)