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: "a proximity aware hierarchical clustering of faces"
  Abstract: In this paper, we propose an unsupervised face clustering algorithm called "Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clusters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging unconstrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain specific deblurring via disentangled representations"):
  with members:
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "exploring disentangled feature representation beyond face identification"
    - "towards open set identity preserving face synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep semantic face deblurring"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "an experimental evaluation of covariates effects on unconstrained face verification"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "l2 constrained softmax loss for discriminative face verification"
    - "level playing field for million scale face recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "a comprehensive analysis of deep learning based representation for face recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning to cluster faces on an affinity graph"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "arcface additive angular margin loss for deep face recognition"
    - "face clustering representation and pairwise constraints"
    - "consensus driven propagation in massive unlabeled data for face recognition"
    - "mask r cnn"
    - "clustering millions of faces by identity"

Connected through hyperedge HE-4 (induced by source paper "linkage based face clustering via graph convolution network"):
  with members:
    - "link prediction based on graph neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "face clustering representation and pairwise constraints"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "level playing field for million scale face recognition"
    - "clustering millions of faces by identity"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "crystal loss and quality pooling for unconstrained face verification and recognition"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "the do s and don ts for cnn based face verification"
    - "faceposenet making a case for landmark free face alignment"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "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-6 (induced by source paper "ring loss convex feature normalization for face recognition"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "range loss for deep face recognition with long tail"

Connected through hyperedge HE-7 (induced by source paper "l2 constrained softmax loss for discriminative face verification"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "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"
    - "aggregated residual transformations for deep neural networks"
    - "deep convolutional neural network features and the original image"

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)