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: "face clustering representation and pairwise constraints"
  Abstract: Clustering face images according to their identity has two important applications: (i) grouping a collection of face images when no external labels are associated with images, and (ii) indexing for efficient large scale face retrieval. The clustering problem is composed of two key parts: face representation and choice of similarity for grouping faces. We first propose a representation based on ResNet, which has been shown to perform very well in image classification problems. Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarity between face images. This allows a dynamic selection of number of clusters and retains pairwise similarity between faces. ConPaC formulates the clustering problem as a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to find an approximate solution for maximizing the posterior probability of the adjacency matrix. Experimental results on two benchmark face datasets (LFW and IJB-B) show that ConPaC outperforms well known clustering algorithms such as k-means, spectral clustering and approximate rank-order. Additionally, our algorithm can naturally incorporate pairwise constraints to obtain a semi-supervised version that leads to improved clustering performance. We also propose an k-NN variant of ConPaC, which has a linear time complexity given a k-NN graph, suitable for large datasets.

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"
    - "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"
    - "learnable pooling with context gating for video classification"

Connected through hyperedge HE-2 (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"
    - "consensus driven propagation in massive unlabeled data for face recognition"
    - "a proximity aware hierarchical clustering of faces"
    - "mask r cnn"
    - "clustering millions of faces by identity"

Connected through hyperedge HE-3 (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"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "a proximity aware hierarchical clustering of faces"
    - "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-4 (induced by source paper "graph representation for face analysis in image collections"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "arcface additive angular margin loss for deep face recognition"
    - "a style based generator architecture for generative adversarial networks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "fine grained head pose estimation without keypoints"
    - "face detection using deep learning an improved faster rcnn approach"
    - "clustering millions of faces by identity"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"):
  with members:
    - "age progression regression by conditional adversarial autoencoder"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "structured feature learning for pose estimation"
    - "learning to refine object segments"
    - "dynamic attention controlled cascaded shape regression exploiting training data augmentation and fuzzy set sample weighting"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"

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)