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: "learning imbalanced datasets with label distribution aware margin loss"
  Abstract: Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound. This loss replaces the standard cross-entropy objective during training and can be applied with prior strategies for training with class-imbalance such as re-weighting or re-sampling. Second, we propose a simple, yet effective, training schedule that defers re-weighting until after the initial stage, allowing the model to learn an initial representation while avoiding some of the complications associated with re-weighting or re-sampling. We test our methods on several benchmark vision tasks including the real-world imbalanced dataset iNaturalist 2018. Our experiments show that either of these methods alone can already improve over existing techniques and their combination achieves even better performance gains.

Connected through hyperedge HE-1 (induced by source paper "learning from multiple experts self paced knowledge distillation for long tailed classification"):
  with members:
    - "imbalance problems in object detection a review"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "learning to reweight examples for robust deep learning"
    - "evolutionary data measures understanding the difficulty of text classification tasks"
    - "dynamic few shot visual learning without forgetting"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-2 (induced by source paper "class imbalanced semi supervised learning"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "imbalanced deep learning by minority class incremental rectification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "manifold regularization with gans for semi supervised learning"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "deep over sampling framework for classifying imbalanced data"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "rethinking data augmentation self supervision and self distillation"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "s4l self supervised semi supervised learning"
    - "meta learning with differentiable convex optimization"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "class balanced loss based on effective number of samples"
    - "self supervised gans via auxiliary rotation loss"
    - "meta learning with latent embedding optimization"
    - "knowledge distillation by on the fly native ensemble"

Connected through hyperedge HE-4 (induced by source paper "does learning require memorization a short tale about a long tail"):
  with members:
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "towards demystifying membership inference attacks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-5 (induced by source paper "debface de biasing face recognition"):
  with members:
    - "max margin class imbalanced learning with gaussian affinity"
    - "generative adversarial minority oversampling"
    - "probabilistic face embeddings"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"
    - "minimax entropy network learning category invariant features for domain adaptation"
    - "class balanced loss based on effective number of samples"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "multi task convolutional neural network for pose invariant face recognition"

Connected through hyperedge HE-6 (induced by source paper "decoupling representation and classifier for long tailed recognition"):
  with members:
    - "max margin class imbalanced learning with gaussian affinity"
    - "a study on action detection in the wild"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "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"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-7 (induced by source paper "distributionally robust neural networks for group shifts on the importance of regularization for worst case generalization"):
  with members:
    - "invariance inducing regularization using worst case transformations suffices to boost accuracy and spatial robustness"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "distributionally robust language modeling"
    - "class balanced loss based on effective number of samples"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "annotation artifacts in natural language inference data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "improved sample complexities for deep networks and robust classification via an all layer margin"):
  with members:
    - "a hessian based complexity measure for deep networks"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "adversarial training can hurt generalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "implicit bias of gradient descent on linear convolutional networks"

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.LG(Machine Learning)