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 search at scale 80 million gallery"
  Abstract: Due to the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to process and search for persons of interest among the billions of shared photos on these websites. Facebook revealed in a 2013 white paper that its users have uploaded more than 250 billion photos, and are uploading 350 million new photos each day. Due to this humongous amount of data, large-scale face search for mining web images is both important and challenging. Despite significant progress in face recognition, searching a large collection of unconstrained face images has not been adequately addressed. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top-k most similar faces using deep features generated from a convolutional neural network. The k candidates are re-ranked by combining similarities from deep features and the COTS matcher. We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images. Experimental results demonstrate that the deep features are competitive with state-of-the-art methods on unconstrained face recognition benchmarks (LFW and IJB-A). Further, the proposed face search system offers an excellent trade-off between accuracy and scalability on datasets consisting of millions of images. Additionally, in an experiment involving searching for face images of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed face search system could find the younger brother's (Dzhokhar Tsarnaev) photo at rank 1 in 1 second on a 5M gallery and at rank 8 in 7 seconds on an 80M gallery.

Connected through hyperedge HE-1 (induced by source paper "dependency aware attention control for unconstrained face recognition with image sets"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep reinforcement learning a brief survey"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "quality aware network for set to set recognition"
    - "deep reinforcement learning an overview"
    - "deep reinforcement learning attention selection for person re identification"
    - "classification with costly features using deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi prototype networks for unconstrained set based face recognition"):
  with members:
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "cosface large margin cosine loss for deep face recognition"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for 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"

Connected through hyperedge HE-3 (induced by source paper "attention control with metric learning alignment for image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "conservative wasserstein training for pose estimation"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "revisiting temporal modeling for video based person reid"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-4 (induced by source paper "racial faces in the wild reducing racial bias by information maximization adaptation network"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "deep visual domain adaptation a survey"
    - "arcface additive angular margin loss for deep face recognition"
    - "supervised cosmos autoencoder learning beyond the euclidean loss"
    - "deep face recognition a survey"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

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"
    - "a proximity aware hierarchical clustering of faces"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a good practice towards top performance of face recognition transferred deep feature fusion"):
  with members:
    - "representation learning by rotating your faces"
    - "l2 constrained softmax loss for discriminative face verification"
    - "squeeze and excitation networks"
    - "towards large pose face frontalization in the wild"
    - "neural aggregation network for video face recognition"
    - "resnet in resnet generalizing residual architectures"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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"
    - "a proximity aware hierarchical clustering of faces"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "unsupervised domain adaptation for face recognition in unlabeled videos"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "neural aggregation network for video face recognition"
    - "unsupervised cross domain image generation"
    - "do we really need to collect millions of faces for effective face recognition"
    - "pixel level domain transfer"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "coupled generative adversarial networks"
    - "view synthesis by appearance flow"

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)