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: "viplfacenet an open source deep face recognition sdk"
  Abstract: Robust face representation is imperative to highly accurate face recognition. In this work, we propose an open source face recognition method with deep representation named as VIPLFaceNet, which is a 10-layer deep convolutional neural network with 7 convolutional layers and 3 fully-connected layers. Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training time and 60% testing time, but achieves 40\% drop in error rate on the real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean accuracy on LFW using one single network. An open-source C++ SDK based on VIPLFaceNet is released under BSD license. The SDK takes about 150ms to process one face image in a single thread on an i7 desktop CPU. VIPLFaceNet provides a state-of-the-art start point for both academic and industrial face recognition applications.

Connected through hyperedge HE-1 (induced by source paper "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"):
  with members:
    - "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "face clustering representation and pairwise constraints"
    - "merge or not learning to group faces via imitation learning"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "maximum likelihood approximate nearest neighbor method in real time image recognition"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learning channel inter dependencies at multiple scales on dense networks for face recognition"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "squeeze and excitation networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a lightened cnn for deep face representation"

Connected through hyperedge HE-5 (induced by source paper "deepvisage making face recognition simple yet with powerful generalization skills"):
  with members:
    - "neural aggregation network for video face recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "triplet probabilistic embedding for face verification and clustering"
    - "triplet similarity embedding for face verification"
    - "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-6 (induced by source paper "contrastive center loss for deep neural networks"):
  with members:
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "wasserstein cnn learning invariant features for nir vis face recognition"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "a light cnn for deep face representation with noisy labels"
    - "shared representation learning for heterogeneous face recognition"

Connected through hyperedge HE-8 (induced by source paper "coupled deep learning for heterogeneous face recognition"):
  with members:
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "a lightened cnn for deep face representation"
    - "facenet a unified embedding for face recognition and clustering"
    - "shared representation learning for heterogeneous face recognition"
    - "trace lasso a trace norm regularization for correlated designs"

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)