human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "autodial automatic domain alignment layers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "learning deep nbnn representations for robust place categorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "use of first and third person views for deep intersection classification")
  Member "addressing appearance change in outdoor robotics with adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "heatnet bridging the day night domain gap in semantic segmentation with thermal images")
  Member "deep learning features at scale for visual place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "multi scale continuous crfs as sequential deep networks for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")
  Member "just dial domain alignment layers for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to see across domains and modalities")
  Member "imagenet pre trained models with batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning embeddings into entropic wasserstein spaces")
  Member "revisiting batch normalization for practical domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross domain few shot classification via learned feature wise transformation")

Question: Please tell me which class the source paper 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)