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: "episodic training for domain generalization"
  Abstract: Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper, we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. Furthermore, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general-purpose feature extractor by explicitly training a feature for robustness to novel problems. This shows that DG training can benefit standard practice in computer vision.

Connected through hyperedge HE-1 (induced by source paper "domain adaptive ensemble learning"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "consistency regularization for generative adversarial networks"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "randaugment practical data augmentation with no separate search"
    - "domain generalization by solving jigsaw puzzles"
    - "semi supervised domain adaptation via minimax entropy"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-2 (induced by source paper "deep domain adversarial image generation for domain generalisation"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "model vulnerability to distributional shifts over image transformation sets"
    - "domain generalization by solving jigsaw puzzles"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "learning generalisable omni scale representations for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "generalizing across domains via cross gradient training"

Connected through hyperedge HE-3 (induced by source paper "explainable deep classification models for domain generalization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "domain generalization by solving jigsaw puzzles"
    - "weakly supervised instance segmentation using class peak response"
    - "excitation dropout encouraging plasticity in deep neural networks"
    - "moment matching for multi source domain adaptation"
    - "adversarial complementary learning for weakly supervised object localization"
    - "generalizing across domains via cross gradient training"

Connected through hyperedge HE-4 (induced by source paper "learn to expect the unexpected probably approximately correct domain generalization"):
  with members:
    - "domain generalization by solving jigsaw puzzles"
    - "learning robust representations by projecting superficial statistics out"
    - "domain generalization via model agnostic learning of semantic features"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "best sources forward domain generalization through source specific nets"
    - "multi source domain adaptation with mixture of experts"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "learning to generalize one sample at a time with self supervision"):
  with members:
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "self supervised generalisation with meta auxiliary learning"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "open set domain adaptation by backpropagation"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-6 (induced by source paper "reducing domain gap via style agnostic networks"):
  with members:
    - "domain generalization using a mixture of multiple latent domains"
    - "exploring the origins and prevalence of texture bias in convolutional neural networks"
    - "srm a style based recalibration module for convolutional neural networks"
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "semi supervised domain adaptation via minimax entropy"
    - "a closer look at few shot classification"
    - "domain generalization via model agnostic learning of semantic features"

Connected through hyperedge HE-7 (induced by source paper "addressing model vulnerability to distributional shifts over image transformation sets"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "random search and reproducibility for neural architecture search"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "semantic adversarial examples"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "autoaugment learning augmentation policies from data"
    - "generalizing across domains via cross gradient training"

Connected through hyperedge HE-8 (induced by source paper "few shot classification on unseen domains by learning disparate modulators"):
  with members:
    - "a closer look at few shot classification"
    - "mxml mixture of meta learners for few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised learning via meta learning"
    - "learning embedding adaptation for few shot learning"

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)