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: "clustering millions of faces by identity"
  Abstract: In this work, we attempt to address the following problem: Given a large number of unlabeled face images, cluster them into the individual identities present in this data. We consider this a relevant problem in different application scenarios ranging from social media to law enforcement. In large-scale scenarios the number of faces in the collection can be of the order of hundreds of million, while the number of clusters can range from a few thousand to millions--leading to difficulties in terms of both run-time complexity and evaluating clustering and per-cluster quality. An efficient and effective Rank-Order clustering algorithm is developed to achieve the desired scalability, and better clustering accuracy than other well-known algorithms such as k-means and spectral clustering. We cluster up to 123 million face images into over 10 million clusters, and analyze the results in terms of both external cluster quality measures (known face labels) and internal cluster quality measures (unknown face labels) and run-time. Our algorithm achieves an F-measure of 0.87 on a benchmark unconstrained face dataset (LFW, consisting of 13K faces), and 0.27 on the largest dataset considered (13K images in LFW, plus 123M distractor images). Additionally, we present preliminary work on video frame clustering (achieving 0.71 F-measure when clustering all frames in the benchmark YouTube Faces dataset). A per-cluster quality measure is developed which can be used to rank individual clusters and to automatically identify a subset of good quality clusters for manual exploration.

Connected through hyperedge HE-1 (induced by source paper "person recognition in personal photo collections"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "pose aware person recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "robust depth based person re identification"
    - "level playing field for million scale face recognition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "probabilistic k means clustering via nonlinear programming"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "on the reconstruction of face images from deep face templates"):
  with members:
    - "pixel deconvolutional networks"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "srefi synthesis of realistic example face images"
    - "improved training of wasserstein gans"
    - "synthesizing normalized faces from facial identity features"
    - "memnet a persistent memory network for image restoration"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-4 (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"
    - "a proximity aware hierarchical clustering of faces"
    - "mask r cnn"

Connected through hyperedge HE-5 (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"
    - "a proximity aware hierarchical clustering of faces"
    - "level playing field for million scale face recognition"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "efficient parameter free clustering using first neighbor relations"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "deep clustering for unsupervised learning of visual features"
    - "multi view low rank sparse subspace clustering"
    - "deep continuous clustering"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Connected through hyperedge HE-7 (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"
    - "face clustering representation and pairwise constraints"
    - "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"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "similarity kernel and clustering via random projection forests"):
  with members:
    - "incorporating deep features in the analysis of tissue microarray images"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"
    - "similarity learning for high dimensional sparse data"
    - "a survey on metric learning for feature vectors and structured data"
    - "a survey on multi view learning"
    - "a tutorial on spectral clustering"

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)