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: "feature transfer learning for deep face recognition with under represented data"
  Abstract: Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such significant portion results in insufficient training data. Training with under-represented data leads to biased classifiers in conventionally-trained deep networks. In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples. A Gaussian prior of the variance is assumed across all subjects and the variance from regular ones are transferred to the under-represented ones. This encourages the under-represented distribution to be closer to the regular distribution. Further, an alternating training regimen is proposed to simultaneously achieve less biased classifiers and a more discriminative feature representation. We conduct ablative study to mimic the under-represented datasets by varying the portion of under-represented classes on the MS-Celeb-1M dataset. Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. Moreover, our feature transfer successfully presents smooth visual interpolation, which conducts disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations such as pose and lighting.

Connected through hyperedge HE-1 (induced by source paper "striking the right balance with uncertainty"):
  with members:
    - "max margin class imbalanced learning with gaussian affinity"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning to reweight examples for robust deep learning"
    - "additive margin softmax for face verification"
    - "linear maximum margin classifier for learning from uncertain data"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "class balanced loss based on effective number of samples"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "exploring the limits of weakly supervised pretraining"
    - "learning to reweight examples for robust deep learning"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "the devil is in the tails fine grained classification in the wild"
    - "focal loss for dense object detection"
    - "class rectification hard mining for imbalanced deep learning"

Connected through hyperedge HE-3 (induced by source paper "max margin class imbalanced learning with gaussian affinity"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "prototypical networks for few shot learning"
    - "squeeze and excitation networks"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-4 (induced by source paper "deep imbalanced learning for face recognition and attribute prediction"):
  with members:
    - "learning towards minimum hyperspherical energy"
    - "learning to reweight examples for robust deep learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "improving facial attribute prediction using semantic segmentation"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "one shot face recognition by promoting underrepresented classes"
    - "class rectification hard mining for imbalanced deep learning"
    - "loss max pooling for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "resembled generative adversarial networks two domains with similar attributes"):
  with members:
    - "mggan solving mode collapse using manifold guided training"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "prototypical networks for few shot learning"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "density estimation using real nvp"

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)