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: "what is the effect of importance weighting in deep learning"
  Abstract: Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning. While the effect of importance weighting is well-characterized for low-capacity misspecified models, little is known about how it impacts over-parameterized, deep neural networks. This work is inspired by recent theoretical results showing that on (linearly) separable data, deep linear networks optimized by SGD learn weight-agnostic solutions, prompting us to ask, for realistic deep networks, for which many practical datasets are separable, what is the effect of importance weighting? We present the surprising finding that while importance weighting impacts models early in training, its effect diminishes over successive epochs. Moreover, while L2 regularization and batch normalization (but not dropout), restore some of the impact of importance weighting, they express the effect via (seemingly) the wrong abstraction: why should practitioners tweak the L2 regularization, and by how much, to produce the correct weighting effect? Our experiments confirm these findings across a range of architectures and datasets.

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 "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning imbalanced datasets with label distribution aware margin loss"):
  with members:
    - "diversity in faces"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "overfitting of neural nets under class imbalance analysis and improvements for segmentation"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "regularized learning for domain adaptation under label shifts"

Connected through hyperedge HE-4 (induced by source paper "fair generative modeling via weak supervision"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "scalable fair clustering"
    - "counterpoint by convolution"
    - "fair clustering through fairlets"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "datasheets for datasets"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "learning beyond human expertise with generative models for dental restorations"

Connected through hyperedge HE-5 (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"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "stabilizing off policy q learning via bootstrapping error reduction"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-7 (induced by source paper "bbn bilateral branch network with cumulative learning for long tailed visual recognition"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "deep learning for fine grained image analysis a survey"
    - "rpc a large scale retail product checkout dataset"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "estimating brain age based on a healthy population with deep learning and structural mri"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gaussian process uncertainty in age estimation as a measure of brain abnormality"
    - "brain age prediction based on resting state functional connectivity patterns using convolutional neural networks"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image 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.LG(Machine Learning)