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: "cross age lfw a database for studying cross age face recognition in unconstrained environments"
  Abstract: Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%. However, we argue that this accuracy may be too optimistic because of some limiting factors. Besides different poses, illuminations, occlusions and expressions, cross-age face is another challenge in face recognition. Different ages of the same person result in large intra-class variations and aging process is unavoidable in real world face verification. However, LFW does not pay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which deliberately searches and selects 3,000 positive face pairs with age gaps to add aging process intra-class variance. Negative pairs with same gender and race are also selected to reduce the influence of attribute difference between positive/negative pairs and achieve face verification instead of attributes classification. We evaluate several metric learning and deep learning methods on the new database. Compared to the accuracy on LFW, the accuracy drops about 10%-17% on CALFW.

Connected through hyperedge HE-1 (induced by source paper "xcos an explainable cosine metric for face verification task"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "improving the interpretability of deep neural networks with knowledge distillation"
    - "ted teaching ai to explain its decisions"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards interpretable face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform"):
  with members:
    - "seesaw net convolution neural network with uneven group convolution"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "searching for mobilenetv3"
    - "airface lightweight and efficient model for face recognition"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep recurrent level set for segmenting brain tumors"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "mis classified vector guided softmax loss for face recognition"):
  with members:
    - "a high efficiency framework for constructing large scale face parsing benchmark"
    - "the devil of face recognition is in the noise"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "support vector guided softmax loss for face recognition"

Connected through hyperedge HE-4 (induced by source paper "face recognition in unconstrained conditions a systematic review"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "face detection through scale friendly deep convolutional networks"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-5 (induced by source paper "attentional feature pair relation networks for accurate face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "pairwise relational networks for face recognition"
    - "comparator networks"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-6 (induced by source paper "atfacegan single face image restoration and recognition from atmospheric turbulence"):
  with members:
    - "unsupervised domain specific deblurring via disentangled representations"
    - "subsampled turbulence removal network"
    - "deep semantic face deblurring"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep face deblurring"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "cross resolution face recognition via prior aided face hallucination and residual knowledge distillation"):
  with members:
    - "low resolution face recognition in the wild via selective knowledge distillation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "adversarial network compression"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "adversarial discriminative heterogeneous face recognition"

Connected through hyperedge HE-8 (induced by source paper "arcface additive angular margin loss for deep face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "iqiyi vid a large dataset for multi modal person identification"
    - "rethinking feature distribution for loss functions in image classification"
    - "learning towards minimum hyperspherical energy"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "comparator networks"
    - "face recognition via centralized coordinate learning"

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)