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: "adaptive deep learning through visual domain localization"
  Abstract: A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision.

Connected through hyperedge HE-1 (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"
    - "generalizing across domains via cross gradient training"
    - "domain generalization with domain specific aggregation modules"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-2 (induced by source paper "tackling partial domain adaptation with self supervision"):
  with members:
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "boosting domain adaptation by discovering latent domains"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "twins two weighted inconsistency reduced networks for partial domain adaptation"
    - "autodial automatic domain alignment layers"

Connected through hyperedge HE-3 (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"
    - "domain generalization with domain specific aggregation modules"
    - "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-4 (induced by source paper "machine vision in the context of robotics a systematic literature review"):
  with members:
    - "dpc net deep pose correction for visual localization"
    - "automatic tool landmark detection for stereo vision in robot assisted retinal surgery"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (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"
    - "generalizing across domains via cross gradient training"
    - "domain generalization with domain specific aggregation modules"
    - "robust place categorization with deep domain generalization"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "domain generalization with domain specific aggregation modules"):
  with members:
    - "best sources forward domain generalization through source specific nets"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "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 "partial adversarial domain adaptation"):
  with members:
    - "deep hashing network for unsupervised domain adaptation"
    - "few shot adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "deep transfer learning with joint adaptation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "simultaneous deep transfer across domains and tasks"

Connected through hyperedge HE-8 (induced by source paper "kitting in the wild through online domain adaptation"):
  with members:
    - "best sources forward domain generalization through source specific nets"
    - "boosting domain adaptation by discovering latent domains"
    - "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)