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: "icub world friendly robots help building good vision data sets"
  Abstract: In this paper we present and start analyzing the iCub World data-set, an object recognition data-set, we acquired using a Human-Robot Interaction (HRI) scheme and the iCub humanoid robot platform. Our set up allows for rapid acquisition and annotation of data with corresponding ground truth. While more constrained in its scopes -- the iCub world is essentially a robotics research lab -- we demonstrate how the proposed data-set poses challenges to current recognition systems. The iCubWorld data-set is publicly available. The data-set can be downloaded from: this http URL

Connected through hyperedge HE-1 (induced by source paper "the icub multisensor datasets for robot and computer vision applications"):
  with members:
    - "core50 a new dataset and benchmark for continuous object recognition"

Connected through hyperedge HE-2 (induced by source paper "lifelong machine learning with deep streaming linear discriminant analysis"):
  with members:
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "revisiting distillation and incremental classifier learning"
    - "efficient lifelong learning with a gem"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-3 (induced by source paper "memory efficient experience replay for streaming learning"):
  with members:
    - "extremely fast decision tree"
    - "continuous learning in single incremental task scenarios"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "continual learning through synaptic intelligence"
    - "measuring catastrophic forgetting in neural networks"
    - "gradient episodic memory for continual learning"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fearnet brain inspired model for incremental learning"

Connected through hyperedge HE-4 (induced by source paper "how important is weight symmetry in backpropagation"):
  with members:
    - "towards biologically plausible deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "neural turing machines"
    - "matconvnet convolutional neural networks for matlab"
    - "distributed representations of words and phrases and their compositionality"

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)