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: "robust place categorization with deep domain generalization"
  Abstract: Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination, and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have been proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior information about the scenario where the robot will operate is available at training time. Unfortunately, in many cases, this assumption does not hold, as we often do not know where a robot will be deployed. To overcome this issue, in this paper, we present an approach that aims at learning classification models able to generalize to unseen scenarios. Specifically, we propose a novel deep learning framework for domain generalization. Our method develops from the intuition that, given a set of different classification models associated to known domains (e.g., corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models. To implement our idea, we exploit recent advances in deep domain adaptation and design a convolutional neural network architecture with novel layers performing a weighted version of batch normalization. Our experiments, conducted on three common datasets for robot place categorization, confirm the validity of our contribution.

Connected through hyperedge HE-1 (induced by source paper "learning to see across domains and modalities"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "generalizing across domains via cross gradient training"
    - "learning from noisy web data with category level supervision"
    - "kitting in the wild through online domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep layer aggregation"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-2 (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-3 (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"
    - "domain generalization with domain specific aggregation modules"

Connected through hyperedge HE-4 (induced by source paper "addressing model vulnerability to distributional shifts over image transformation sets"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "random search and reproducibility for neural architecture search"
    - "episodic training for domain generalization"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "semantic adversarial examples"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-5 (induced by source paper "deduce diverse scene detection methods in unseen challenging environments"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "wide residual networks"

Connected through hyperedge HE-6 (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"
    - "domain generalization with domain specific aggregation modules"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-7 (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"
    - "adaptive deep learning through visual domain localization"
    - "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-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"
    - "adaptive deep learning through visual domain localization"
    - "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.RO(Robotics)