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: "triplet similarity embedding for face verification"
  Abstract: In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods. The proposed algorithm couples a deep CNN-based approach with a low-dimensional discriminative embedding learnt using triplet similarity constraints in a large margin fashion. Aside from yielding performance improvement, this embedding provides significant advantages in terms of memory and post-processing operations like hashing and visualization. Experiments on the IJB-A dataset show that the proposed algorithm outperforms state of the art methods in verification and identification metrics, while requiring less training time.

Connected through hyperedge HE-1 (induced by source paper "angular learning toward discriminative embedded features"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "one shot face recognition by promoting underrepresented classes"

Connected through hyperedge HE-2 (induced by source paper "face recognition from traditional to deep learning methods"):
  with members:
    - "representation learning by rotating your faces"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-3 (induced by source paper "racial faces in the wild reducing racial bias by information maximization adaptation network"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "deep visual domain adaptation a survey"
    - "arcface additive angular margin loss for deep face recognition"
    - "supervised cosmos autoencoder learning beyond the euclidean loss"
    - "deep face recognition a survey"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "real world anomaly detection in surveillance videos"):
  with members:
    - "tube convolutional neural network t cnn for action detection in videos"
    - "deep image retrieval learning global representations for image search"
    - "learning temporal regularity in video sequences"
    - "video2gif automatic generation of animated gifs from video"
    - "theano a python framework for fast computation of mathematical expressions"
    - "deep feature learning with relative distance comparison for person re identification"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning deep representations of appearance and motion for anomalous event detection"

Connected through hyperedge HE-5 (induced by source paper "von mises fisher mixture model based deep learning application to face verification"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "face aging with conditional generative adversarial networks"
    - "l2 constrained softmax loss for discriminative face verification"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"
    - "automatic face image quality prediction"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"

Connected through hyperedge HE-6 (induced by source paper "a good practice towards top performance of face recognition transferred deep feature fusion"):
  with members:
    - "representation learning by rotating your faces"
    - "l2 constrained softmax loss for discriminative face verification"
    - "squeeze and excitation networks"
    - "towards large pose face frontalization in the wild"
    - "neural aggregation network for video face recognition"
    - "resnet in resnet generalizing residual architectures"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-7 (induced by source paper "deepvisage making face recognition simple yet with powerful generalization skills"):
  with members:
    - "neural aggregation network for video face recognition"
    - "viplfacenet an open source deep face recognition sdk"
    - "do we really need to collect millions of faces for effective face recognition"
    - "triplet probabilistic embedding for face verification and clustering"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "an all in one convolutional neural network for face analysis"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "umdfaces an annotated face dataset for training deep networks"

Connected through hyperedge HE-8 (induced by source paper "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "triplet probabilistic embedding for face verification and clustering"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

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)