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: "umdfaces an annotated face dataset for training deep networks"
  Abstract: Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large datasets are maintained by private companies and are not publicly available. The academic computer vision community needs larger and more varied datasets to make further progress. #R##N#In this paper we introduce a new face dataset, called UMDFaces, which has 367,888 annotated faces of 8,277 subjects. We also introduce a new face recognition evaluation protocol which will help advance the state-of-the-art in this area. We discuss how a large dataset can be collected and annotated using human annotators and deep networks. We provide human curated bounding boxes for faces. We also provide estimated pose (roll, pitch and yaw), locations of twenty-one key-points and gender information generated by a pre-trained neural network. In addition, the quality of keypoint annotations has been verified by humans for about 115,000 images. Finally, we compare the quality of the dataset with other publicly available face datasets at similar scales.

Connected through hyperedge HE-1 (induced by source paper "investigating the impact of inclusion in face recognition training data on individual face identification"):
  with members:
    - "diversity in faces"
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "level playing field for million scale face recognition"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-2 (induced by source paper "deep heterogeneous hashing for face video retrieval"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "is second order information helpful for large scale visual recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-3 (induced by source paper "single unit status in deep convolutional neural network codes for face identification sparseness redefined"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "how convolutional neural network see the world a survey of convolutional neural network visualization methods"
    - "the do s and don ts for cnn based face verification"
    - "triplet probabilistic embedding for face verification and clustering"
    - "an all in one convolutional neural network for face analysis"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "unconstrained face verification using deep cnn features"

Connected through hyperedge HE-4 (induced by source paper "deep learning for face recognition a critical analysis"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face attention network an effective face detector for the occluded faces"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "the do s and don ts for cnn based face verification"

Connected through hyperedge HE-5 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "pyramidbox a context assisted single shot face detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-6 (induced by source paper "low resolution face recognition in the wild via selective knowledge distillation"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "low resolution face recognition in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"
    - "data distillation towards omni supervised learning"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"

Connected through hyperedge HE-7 (induced by source paper "diversity in faces"):
  with members:
    - "the devil of face recognition is in the noise"
    - "women also snowboard overcoming bias in captioning models"
    - "every smile is unique landmark guided diverse smile generation"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation of both diseased and healthy skin from clinical photographs in a primary care setting"
    - "bias mitigation post processing for individual and group fairness"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-8 (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"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"

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)