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: "megaface a million faces for recognition at scale"
  Abstract: Recent face recognition experiments on the LFW benchmark show that face recognition is performing stunningly well, surpassing human recognition rates. In this paper, we study face recognition at scale. Specifically, we have collected from Flickr a \textbf{Million} faces and evaluated state of the art face recognition algorithms on this dataset. We found that the performance of algorithms varies--while all perform great on LFW, once evaluated at scale recognition rates drop drastically for most algorithms. Interestingly, deep learning based approach by \cite{schroff2015facenet} performs much better, but still gets less robust at scale. We consider both verification and identification problems, and evaluate how pose affects recognition at scale. Moreover, we ran an extensive human study on Mechanical Turk to evaluate human recognition at scale, and report results. All the photos are creative commons photos and is released at \small{\url{this http URL}} for research and further experiments.

Connected through hyperedge HE-1 (induced by source paper "advhat real world adversarial attack on arcface face id system"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "adversarial patch"
    - "cosface large margin cosine loss for deep face recognition"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "large scale multi modal person identification in real unconstrained environments"):
  with members:
    - "iqiyi vid a large dataset for multi modal person identification"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "voxceleb2 deep speaker recognition"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-3 (induced by source paper "airface lightweight and efficient model for face recognition"):
  with members:
    - "retinaface single stage dense face localisation in the wild"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "cbam convolutional block attention module"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "iqiyi vid a large dataset for multi modal person identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person search in videos with one portrait through visual and temporal links"
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "voxceleb2 deep speaker recognition"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"

Connected through hyperedge HE-5 (induced by source paper "sphereface deep hypersphere embedding for face recognition"):
  with members:
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "sparsifying neural network connections for face recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-6 (induced by source paper "cross age lfw a database for studying cross age face recognition in unconstrained environments"):
  with members:
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "yfcc100m the new data in multimedia research"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning face representation from scratch"

Connected through hyperedge HE-7 (induced by source paper "frankenstein learning deep face representations using small data"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "do we really need to collect millions of faces for effective face recognition"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "semantic pose using deep networks trained on synthetic rgb d"
    - "deepid3 face recognition with very deep neural networks"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"

Connected through hyperedge HE-8 (induced by source paper "umdfaces an annotated face dataset for training deep networks"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "do we really need to collect millions of faces for effective face recognition"
    - "deep networks with stochastic depth"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "triplet similarity embedding for face verification"
    - "an all in one convolutional neural network for face analysis"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deepid3 face recognition with very deep neural networks"

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)