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: "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
  Abstract: Surveillance cameras today often capture NIR (near infrared) images in low-light environments. However, most face datasets accessible for training and verification are only collected in the VIS (visible light) spectrum. It remains a challenging problem to match NIR to VIS face images due to the different light spectrum. Recently, breakthroughs have been made for VIS face recognition by applying deep learning on a huge amount of labeled VIS face samples. The same deep learning approach cannot be simply applied to NIR face recognition for two main reasons: First, much limited NIR face images are available for training compared to the VIS spectrum. Second, face galleries to be matched are mostly available only in the VIS spectrum. In this paper, we propose an approach to extend the deep learning breakthrough for VIS face recognition to the NIR spectrum, without retraining the underlying deep models that see only VIS faces. Our approach consists of two core components, cross-spectral hallucination and low-rank embedding, to optimize respectively input and output of a VIS deep model for cross-spectral face recognition. Cross-spectral hallucination produces VIS faces from NIR images through a deep learning approach. Low-rank embedding restores a low-rank structure for faces deep features across both NIR and VIS spectrum. We observe that it is often equally effective to perform hallucination to input NIR images or low-rank embedding to output deep features for a VIS deep model for cross-spectral recognition. When hallucination and low-rank embedding are deployed together, we observe significant further improvement; we obtain state-of-the-art accuracy on the CASIA NIR-VIS v2.0 benchmark, without the need at all to re-train the recognition system.

Connected through hyperedge HE-1 (induced by source paper "effective domain knowledge transfer with soft fine tuning"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep layer aggregation"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "wasserstein cnn learning invariant features for nir vis face recognition"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-2 (induced by source paper "polarimetric thermal to visible face verification via self attention guided synthesis"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "spectral normalization for generative adversarial networks"
    - "thermal to visible synthesis of face images using multiple regions"
    - "deep cross polarimetric thermal to visible face recognition"
    - "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "polarimetric thermal to visible face verification via attribute preserved synthesis"):
  with members:
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "thermal to visible synthesis of face images using multiple regions"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-4 (induced by source paper "deeptio a deep thermal inertial odometry with visual hallucination"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "keyframe based direct thermal inertial odometry"
    - "distilling knowledge from a deep pose regressor network"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-5 (induced by source paper "face recognition a novel multi level taxonomy based survey"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "ring loss convex feature normalization for face recognition"
    - "a double deep spatio angular learning framework for light field based face recognition"
    - "face recognition via centralized coordinate learning"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-6 (induced by source paper "dual variational generation for low shot heterogeneous face recognition"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "high fidelity face manipulation with extreme pose and expression"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "arcface additive angular margin loss for deep face recognition"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"
    - "disentangled variational representation for heterogeneous face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-7 (induced by source paper "unsupervised cross spectral stereo matching by learning to synthesize"):
  with members:
    - "learning cross modal deep representations for robust pedestrian detection"
    - "unsupervised learning of depth and ego motion from video"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-8 (induced by source paper "pose agnostic cross spectral hallucination via disentangling independent factors"):
  with members:
    - "high fidelity face manipulation with extreme pose and expression"
    - "dual variational generation for low shot heterogeneous face recognition"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "disentangled variational representation for heterogeneous face recognition"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "adversarial discriminative heterogeneous face recognition"
    - "unpaired image to image translation using cycle consistent 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)