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 with domain specific aggregation modules"
  Abstract: Visual recognition systems are meant to work in the real world. For this to happen, they must work robustly in any visual domain, and not only on the data used during training. Within this context, a very realistic scenario deals with domain generalization, i.e. the ability to build visual recognition algorithms able to work robustly in several visual domains, without having access to any information about target data statistic. This paper contributes to this research thread, proposing a deep architecture that maintains separated the information about the available source domains data while at the same time leveraging over generic perceptual information. We achieve this by introducing domain-specific aggregation modules that through an aggregation layer strategy are able to merge generic and specific information in an effective manner. Experiments on two different benchmark databases show the power of our approach, reaching the new state of the art in domain generalization.

Connected through hyperedge HE-1 (induced by source paper "learning to generalize one sample at a time with self supervision"):
  with members:
    - "episodic training for domain generalization"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "towards shape biased unsupervised representation learning for domain generalization"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-3 (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-4 (induced by source paper "domain generalization by solving jigsaw puzzles"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "partial adversarial domain adaptation"
    - "image reassembly combining deep learning and shortest path problem"
    - "adaptive deep learning through visual domain localization"
    - "generalizing across domains via cross gradient training"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-5 (induced by source paper "adagraph unifying predictive and continuous domain adaptation through graphs"):
  with members:
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "deep visual domain adaptation a survey"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "adaptive deep learning through visual domain localization"
    - "kitting in the wild through online domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unified deep supervised domain adaptation and generalization"

Connected through hyperedge HE-6 (induced by source paper "dense feature aggregation and pruning for rgbt tracking"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "target aware deep tracking"
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "deep attentive tracking via reciprocative learning"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "end to end representation learning for correlation filter based tracking"
    - "deep layer aggregation"

Connected through hyperedge HE-7 (induced by source paper "adversarial target invariant representation learning for domain generalization"):
  with members:
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "on learning invariant representation for domain adaptation"
    - "algorithms and theory for multiple source adaptation"
    - "stabilizing gan training with multiple random projections"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "unified deep supervised domain adaptation and generalization"
    - "deeper broader and artier domain generalization"

Connected through hyperedge HE-8 (induced by source paper "hallucinating agnostic images to generalize across domains"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "multi component image translation for deep domain generalization"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "adaptive deep learning through visual domain localization"
    - "generalizing across domains via cross gradient training"
    - "robust place categorization with deep domain generalization"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "cycada cycle consistent adversarial domain adaptation"

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)