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: "deep imbalanced learning for face recognition and attribute prediction"
  Abstract: Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary deep learning methods typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain inter-cluster margins both within and between classes. This tight constraint effectively reduces the class imbalance inherent in the local data neighborhood, thus carving much more balanced class boundaries locally. We show that it is easy to deploy angular margins between the cluster distributions on a hypersphere manifold. Such learned Cluster-based Large Margin Local Embedding (CLMLE), when combined with a simple k-nearest cluster algorithm, shows significant improvements in accuracy over existing methods on both face recognition and face attribute prediction tasks that exhibit imbalanced class distribution.

Connected through hyperedge HE-1 (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-2 (induced by source paper "on class imbalance and background filtering in visual relationship detection"):
  with members:
    - "graph r cnn for scene graph generation"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "visual relationship prediction via label clustering and incorporation of depth information"
    - "image generation from scene graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "on calibration of modern neural networks"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (induced by source paper "dynamic curriculum learning for imbalanced data classification"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "imbalanced deep learning by minority class incremental rectification"
    - "localization guided learning for pedestrian attribute recognition"
    - "class rectification hard mining for imbalanced deep learning"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"

Connected through hyperedge HE-4 (induced by source paper "scalable evaluation and improvement of document set expansion via neural positive unlabeled learning"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "revisiting sample selection approach to positive unlabeled learning turning unlabeled data into positive rather than negative"
    - "classification from positive unlabeled and biased negative data"
    - "alternate estimation of a classifier and the class prior from positive and unlabeled data"
    - "imbalanced deep learning by minority class incremental rectification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "class prior estimation for learning from positive and unlabeled data"

Connected through hyperedge HE-5 (induced by source paper "deep imbalanced attribute classification using visual attention aggregation"):
  with members:
    - "deep visual attention prediction"
    - "harmonious attention network for person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "focal loss for dense object detection"
    - "curriculum learning for multi task classification of visual attributes"
    - "order free rnn with visual attention for multi label classification"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-6 (induced by source paper "instance level facial attributes transfer with geometry aware flow"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "unsupervised image to image translation networks"
    - "neural face editing with intrinsic image disentangling"

Connected through hyperedge HE-7 (induced by source paper "consensus driven propagation in massive unlabeled data for face recognition"):
  with members:
    - "the devil of face recognition is in the noise"
    - "accelerated training for massive classification via dynamic class selection"
    - "arcface additive angular margin loss for deep face recognition"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "learning transferable architectures for scalable image recognition"
    - "mix and match tuning for self supervised semantic segmentation"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"

Connected through hyperedge HE-8 (induced by source paper "the devil of face recognition is in the noise"):
  with members:
    - "deep learning is robust to massive label noise"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "consensus driven propagation in massive unlabeled data for face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "level playing field for million scale face recognition"
    - "residual attention network for image classification"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)