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 "kitting in the wild through online domain adaptation"):
  Abstract: Technological developments call for increasing perception and action capabilities of robots. Among other skills, vision systems that can adapt to any possible change in the working conditions are needed. Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms. In this work we focus on robotic kitting in unconstrained scenarios. As a first contribution, we present a new visual dataset for the kitting task. Differently from standard object recognition datasets, we provide images of the same objects acquired under various conditions where camera, illumination and background are changed. This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified. Our second contribution is a novel online adaptation algorithm for deep models, based on batch-normalization layers, which allows to continuously adapt a model to the current working conditions. Differently from standard domain adaptation algorithms, it does not require any image from the target domain at training time. We benchmark the performance of the algorithm on the proposed dataset, showing its capability to fill the gap between the performances of a standard architecture and its counterpart adapted offline to the given target domain.

Includes members:
  Member "best sources forward domain generalization through source specific nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learn to expect the unexpected probably approximately correct domain generalization")
  Member "boosting domain adaptation by discovering latent domains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering")
  Member "adaptive deep learning through visual domain localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain generalization by solving jigsaw puzzles")
  Member "robust place categorization with deep domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to see across domains and modalities")
  Member "deeper broader and artier domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  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 "icarl incremental classifier and representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  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)