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: "range loss for deep face recognition with long tail"
  Abstract: Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities. To train such a well-designed deep network, tremendous amounts of data is indispensable. Long tail distribution specifically refers to the fact that a small number of generic entities appear frequently while other objects far less existing. Considering the existence of long tail distribution of the real world data, large but uniform distributed data are usually hard to retrieve. Empirical experiences and analysis show that classes with more samples will pose greater impact on the feature learning process and inversely cripple the whole models feature extracting ability on tail part data. Contrary to most of the existing works that alleviate this problem by simply cutting the tailed data for uniform distributions across the classes, this paper proposes a new loss function called range loss to effectively utilize the whole long tailed data in training process. More specifically, range loss is designed to reduce overall intra-personal variations while enlarging inter-personal differences within one mini-batch simultaneously when facing even extremely unbalanced data. The optimization objective of range loss is the $k$ greatest range's harmonic mean values in one class and the shortest inter-class distance within one batch. Extensive experiments on two famous and challenging face recognition benchmarks (Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) not only demonstrate the effectiveness of the proposed approach in overcoming the long tail effect but also show the good generalization ability of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search for deep face recognition"):
  with members:
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "a performance comparison of loss functions for deep face recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "a performance comparison of loss functions for deep face recognition"):
  with members:
    - "minimum margin loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "low resolution face recognition in the wild via selective knowledge distillation"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "low resolution face recognition in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"
    - "data distillation towards omni supervised learning"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"

Connected through hyperedge HE-4 (induced by source paper "generative one shot face recognition"):
  with members:
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "sphereface deep hypersphere embedding for face recognition"
    - "good semi supervised learning that requires a bad gan"
    - "few shot adversarial domain adaptation"
    - "one shot face recognition by promoting underrepresented classes"
    - "generative adversarial residual pairwise networks for one shot learning"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-5 (induced by source paper "unknown identity rejection loss utilizing unlabeled data for face recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "iqiyi vid a large dataset for multi modal person identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep co training for semi supervised image recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-6 (induced by source paper "recognition in unseen domains domain generalization via universal non volume preserving models"):
  with members:
    - "generalizing across domains via cross gradient training"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "image to image translation for domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "adversarial discriminative domain adaptation"
    - "densely connected convolutional networks"
    - "coupled generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "face recognition from traditional to deep learning methods"):
  with members:
    - "representation learning by rotating your faces"
    - "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"
    - "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-8 (induced by source paper "arcface additive angular margin loss for deep face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "iqiyi vid a large dataset for multi modal person identification"
    - "rethinking feature distribution for loss functions in image classification"
    - "learning towards minimum hyperspherical energy"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "comparator networks"
    - "face recognition via centralized coordinate learning"

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)