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: "deeper broader and artier domain generalization"
  Abstract: The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has a clear motivation in contexts where there are target domains with distinct characteristics, yet sparse data for training. For example recognition in sketch images, which are distinctly more abstract and rarer than photos. Nevertheless, DG methods have primarily been evaluated on photo-only benchmarks focusing on alleviating the dataset bias where both problems of domain distinctiveness and data sparsity can be minimal. We argue that these benchmarks are overly straightforward, and show that simple deep learning baselines perform surprisingly well on them. In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG benchmark dataset covering photo, sketch, cartoon and painting domains. This is both more practically relevant, and harder (bigger domain shift) than existing benchmarks. The results show that our method outperforms existing DG alternatives, and our dataset provides a more significant DG challenge to drive future research.

Connected through hyperedge HE-1 (induced by source paper "cross domain few shot classification via learned feature wise transformation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "feature critic networks for heterogeneous domain generalization"
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "progressive domain adaptation for object detection"
    - "a closer look at few shot classification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image to image translation with bi directional feature transformation"

Connected through hyperedge HE-2 (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"
    - "episodic training for domain generalization"
    - "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"

Connected through hyperedge HE-3 (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"
    - "episodic training for domain generalization"
    - "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"

Connected through hyperedge HE-4 (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"
    - "episodic training for domain generalization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "towards universal representation learning for deep face recognition"):
  with members:
    - "towards universal object detection by domain attention"
    - "probabilistic face embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "additive margin softmax for face verification"
    - "on calibration of modern neural networks"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-6 (induced by source paper "mind the gap enlarging the domain gap in open set domain adaptation"):
  with members:
    - "attract or distract exploit the margin of open set"
    - "weakly supervised open set domain adaptation by dual domain collaboration"
    - "open set domain adaptation by backpropagation"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "learning unbiased representations via mutual information backpropagation"):
  with members:
    - "on variational bounds of mutual information"
    - "flexibly fair representation learning by disentanglement"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "beyond local nash equilibria for adversarial networks"
    - "invariant representations without adversarial training"
    - "learning not to learn training deep neural networks with biased data"
    - "mitigating unwanted biases with adversarial learning"
    - "generalizing across domains via cross gradient training"

Connected through hyperedge HE-8 (induced by source paper "out of distribution generalization via risk extrapolation rex"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "achieving robustness in the wild via adversarial mixing with disentangled representations"
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "do imagenet classifiers generalize to imagenet"
    - "a closer look at few shot classification"
    - "a fourier perspective on model robustness in computer vision"

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)