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: "ring loss convex feature normalization for face recognition"
  Abstract: We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax. We argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multi-class problem equally well. The direct approach to feature normalization through the hard normalization operation results in a non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features. We apply Ring loss to large-scale face recognition problems and present results on LFW, the challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss outperforms strong baselines, matches state-of-the-art performance on IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We also outperform strong baselines in handling extremely low resolution face matching.

Connected through hyperedge HE-1 (induced by source paper "an open set recognition and few shot learning dataset for audio event classification in domestic environments"):
  with members:
    - "deep face recognition a survey"
    - "training neural audio classifiers with few data"
    - "recent advances in open set recognition a survey"
    - "look listen and learn"
    - "audio scene classification with deep recurrent neural networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-2 (induced by source paper "biometric recognition using deep learning a survey"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "thirdeye triplet based iris recognition without normalization"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deepiris iris recognition using a deep learning approach"
    - "fingernet pushing the limits of fingerprint recognition using convolutional neural network"
    - "generating talking face landmarks from speech"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "dense classification and implanting for few shot learning"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "rethinking feature distribution for loss functions in image classification"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "path aggregation network for instance segmentation"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-4 (induced by source paper "feature selective anchor free module for single shot object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bounding box regression with uncertainty for accurate object detection"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "seeing small faces from robust anchor s perspective"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "detnet a backbone network for object detection"
    - "sface an efficient network for face detection in large scale variations"
    - "videos as space time region graphs"

Connected through hyperedge HE-5 (induced by source paper "frustratingly easy person re identification generalizing person re id in practice"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "eanet enhancing alignment for cross domain person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "batch instance normalization for adaptively style invariant neural networks"

Connected through hyperedge HE-6 (induced by source paper "noise tolerant paradigm for training face recognition cnns"):
  with members:
    - "the devil of face recognition is in the noise"
    - "deep learning is robust to massive label noise"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "masking a new perspective of noisy supervision"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-7 (induced by source paper "domain agnostic learning with disentangled representations"):
  with members:
    - "moment matching for multi source domain adaptation"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "domain generalization via conditional invariant representation"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "dida disentangled synthesis for domain adaptation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "drit diverse image to image translation via disentangled representations"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "adacos adaptively scaling cosine logits for effectively learning deep face representations"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "cosface large margin cosine 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"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "rethinking feature discrimination and polymerization for large scale recognition"

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)