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: "best sources forward domain generalization through source specific nets"
  Abstract: A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation methods have been proposed to address this issue. While these approaches have considered the single source-single target scenario, it is plausible to have multiple sources and require adaptation to any possible target domain. This last scenario, named Domain Generalization (DG), is the focus of our work. Differently from previous DG methods which learn domain invariant representations from source data, we design a deep network with multiple domain-specific classifiers, each associated to a source domain. At test time we estimate the probabilities that a target sample belongs to each source domain and exploit them to optimally fuse the classifiers predictions. To further improve the generalization ability of our model, we also introduced a domain agnostic component supporting the final classifier. Experiments on two public benchmarks demonstrate the power of our approach.

Connected through hyperedge HE-1 (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"
    - "domain generalization via model agnostic learning of semantic features"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "multi source domain adaptation with mixture of experts"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "mfas multimodal fusion architecture search"
    - "task2vec task embedding for meta learning"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"

Connected through hyperedge HE-3 (induced by source paper "learning robust global representations by penalizing local predictive power"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "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"
    - "domain invariant projection learning for zero shot recognition"
    - "deep visual domain adaptation a survey"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-4 (induced by source paper "episodic training for domain generalization"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "generalizing across domains via cross gradient training"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "deep mutual learning"
    - "learning to generalize meta learning for domain generalization"
    - "prototypical networks for few shot learning"
    - "learning multiple visual domains with residual adapters"

Connected through hyperedge HE-5 (induced by source paper "learning robust representations by projecting superficial statistics out"):
  with members:
    - "invariant representations without adversarial training"
    - "generalizing across domains via cross gradient training"
    - "hallucinating agnostic images to generalize across domains"
    - "domain adaptation for visual applications a comprehensive survey"
    - "learning to generalize meta learning for domain generalization"
    - "measuring the tendency of cnns to learn surface statistical regularities"
    - "select additive learning improving generalization in multimodal sentiment analysis"
    - "domain generalization for object recognition with multi task autoencoders"

Connected through hyperedge HE-6 (induced by source paper "domain generalization with domain specific aggregation modules"):
  with members:
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "adaptive deep learning through visual domain localization"
    - "robust place categorization with deep domain generalization"
    - "autodial automatic domain alignment layers"
    - "deep hashing network for unsupervised domain adaptation"
    - "learning to generalize meta learning for domain generalization"
    - "deep layer aggregation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "kitting in the wild through online domain adaptation"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "adaptive deep learning through visual domain localization"
    - "robust place categorization with deep domain generalization"
    - "deeper broader and artier domain generalization"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "icarl incremental classifier and representation learning"
    - "revisiting batch normalization for practical domain adaptation"
    - "online open world 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.CV(Computer Vision and Pattern Recognition)