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: "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.

Connected through hyperedge HE-1 (induced by source paper "multi step online unsupervised domain adaptation"):
  with members:
    - "unsupervised domain adaptation using graph transduction games"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "return of frustratingly easy domain adaptation"
    - "learning transferable features with deep adaptation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (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"
    - "robust place categorization with deep domain generalization"
    - "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-3 (induced by source paper "online adaptation through meta learning for stereo depth estimation"):
  with members:
    - "real time self adaptive deep stereo"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "boosting domain adaptation by discovering latent domains"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised domain adaptation using feature whitening and consensus loss"):
  with members:
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "boosting domain adaptation by discovering latent domains"
    - "decorrelated batch normalization"
    - "deep hashing network for unsupervised domain adaptation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "self ensembling for visual domain adaptation"

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"
    - "domain generalization with domain specific aggregation modules"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unified deep supervised domain adaptation and generalization"

Connected through hyperedge HE-6 (induced by source paper "knowledge is never enough towards web aided deep open world recognition"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "incremental learning for robot perception through hri"
    - "icarl incremental classifier and representation learning"
    - "identity mappings in deep residual networks"
    - "online open world recognition"
    - "towards open world recognition"
    - "deep residual learning for image 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)