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: "coupled deep learning for heterogeneous face recognition"
  Abstract: Heterogeneous face matching is a challenge issue in face recognition due to large domain difference as well as insufficient pairwise images in different modalities during training. This paper proposes a coupled deep learning (CDL) approach for the heterogeneous face matching. CDL seeks a shared feature space in which the heterogeneous face matching problem can be approximately treated as a homogeneous face matching problem. The objective function of CDL mainly includes two parts. The first part contains a trace norm and a block-diagonal prior as relevance constraints, which not only make unpaired images from multiple modalities be clustered and correlated, but also regularize the parameters to alleviate overfitting. An approximate variational formulation is introduced to deal with the difficulties of optimizing low-rank constraint directly. The second part contains a cross modal ranking among triplet domain specific images to maximize the margin for different identities and increase data for a small amount of training samples. Besides, an alternating minimization method is employed to iteratively update the parameters of CDL. Experimental results show that CDL achieves better performance on the challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF), which significantly outperforms state-of-the-art heterogeneous face recognition methods.

Connected through hyperedge HE-1 (induced by source paper "relational deep feature learning for heterogeneous face recognition"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "videos as space time region graphs"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks"):
  with members:
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "deepcas an end to end predictor of information cascades"
    - "information diffusion in social networks in two phases"
    - "revisiting semi supervised learning with graph embeddings"
    - "spreading processes in multilayer networks"
    - "a survey of heterogeneous information network analysis"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (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-4 (induced by source paper "enhancing the discriminative feature learning for visible thermal cross modality person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "learning two branch neural networks for image text matching tasks"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-5 (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-6 (induced by source paper "facial feature embedded cyclegan for vis nir translation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "adversarial discriminative heterogeneous face recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "docface id document to selfie matching"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "docface matching id document photos to selfies"
    - "additive margin softmax for face verification"
    - "large scale bisample learning on id vs spot face recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"

Connected through hyperedge HE-8 (induced by source paper "disentangled variational representation for heterogeneous face recognition"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "adversarial discriminative heterogeneous face recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "wasserstein cnn learning invariant features for nir vis face recognition"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "deep coral correlation alignment for deep domain adaptation"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"

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)