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: "one shot face recognition by promoting underrepresented classes"
  Abstract: In this paper, we study the problem of training large-scale face identification model with imbalanced training data. This problem naturally exists in many real scenarios including large-scale celebrity recognition, movie actor annotation, etc. Our solution contains two components. First, we build a face feature extraction model, and improve its performance, especially for the persons with very limited training samples, by introducing a regularizer to the cross entropy loss for the multi-nomial logistic regression (MLR) learning. This regularizer encourages the directions of the face features from the same class to be close to the direction of their corresponding classification weight vector in the logistic regression. Second, we build a multi-class classifier using MLR on top of the learned face feature extraction model. Since the standard MLR has poor generalization capability for the one-shot classes even if these classes have been oversampled, we propose a novel supervision signal called underrepresented-classes promotion loss, which aligns the norms of the weight vectors of the one-shot classes (a.k.a. underrepresented-classes) to those of the normal classes. In addition to the original cross entropy loss, this new loss term effectively promotes the underrepresented classes in the learned model and leads to a remarkable improvement in face recognition performance. #R##N#We test our solution on the MS-Celeb-1M low-shot learning benchmark task. Our solution recognizes 94.89% of the test images at the precision of 99\% for the one-shot classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017.

Connected through hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "fairface face attribute dataset for balanced race gender and age"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning"):
  with members:
    - "a study on action detection in the wild"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "dynamic curriculum learning for imbalanced data classification"
    - "decoupling representation and classifier for long tailed recognition"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "meta weight net learning an explicit mapping for sample weighting"

Connected through hyperedge HE-3 (induced by source paper "towards fair cross domain adaptation via generative learning"):
  with members:
    - "diversity with cooperation ensemble methods for few shot classification"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "deep visual domain adaptation a survey"
    - "partial adversarial domain adaptation"
    - "bagan data augmentation with balancing gan"
    - "low shot learning from imaginary data"
    - "the frontiers of fairness in machine learning"

Connected through hyperedge HE-4 (induced by source paper "rethinking softmax cross entropy loss for adversarial robustness"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-5 (induced by source paper "to balance or not to balance a simple yet effective approach for learning with long tailed distributions"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "boosting few shot visual learning with self supervision"
    - "dynamic curriculum learning for imbalanced data classification"
    - "self supervised generalisation with meta auxiliary learning"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "revisiting self supervised visual representation learning"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-6 (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"
    - "generative adversarial residual pairwise networks for one shot learning"
    - "range loss for deep face recognition with long tail"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-7 (induced by source paper "large scale incremental learning"):
  with members:
    - "end to end incremental learning"
    - "a strategy for an uncompromising incremental learner"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "encoder based lifelong learning"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "gradient episodic memory for continual learning"
    - "icarl incremental classifier and representation learning"

Connected through hyperedge HE-8 (induced by source paper "angular learning toward discriminative embedded features"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)