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: "domain generalization via model agnostic learning of semantic features"
  Abstract: Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics. We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we introduce two complementary losses which explicitly regularize the semantic structure of the feature space. Globally, we align a derived soft confusion matrix to preserve general knowledge about inter-class relationships. Locally, we promote domain-independent class-specific cohesion and separation of sample features with a metric-learning component. The effectiveness of our method is demonstrated with new state-of-the-art results on two common object recognition benchmarks. Our method also shows consistent improvement on a medical image segmentation task.

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"
    - "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-2 (induced by source paper "learning cross domain generalizable features by representation disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "domain agnostic learning with disentangled representations"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "mixmatch a holistic approach to semi supervised learning"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"
    - "a unified feature disentangler for multi domain image translation and manipulation"

Connected through hyperedge HE-3 (induced by source paper "learn to expect the unexpected probably approximately correct domain generalization"):
  with members:
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "learning robust representations by projecting superficial statistics out"
    - "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-4 (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"
    - "episodic training for domain generalization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "adversarial pyramid network for video domain generalization"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "feature critic networks for heterogeneous domain generalization"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "spatiotemporal pyramid network for video action recognition"
    - "generalizing across domains via cross gradient training"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-6 (induced by source paper "open cross domain visual search"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "hyperspherical prototype networks"
    - "unsupervised open domain recognition by semantic discrepancy minimization"
    - "semantic aware knowledge preservation for zero shot sketch based image retrieval"
    - "domain generalization by solving jigsaw puzzles"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "moment matching for multi source domain adaptation"
    - "deep cross modality adaptation via semantics preserving adversarial learning for sketch based 3d shape retrieval"

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)