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: "fine grained continual learning"
  Abstract: Robotic vision is a field where continual learning can play a significant role. An embodied agent operating in a complex environment subject to frequent and unpredictable changes is required to learn and adapt continuously. In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never seen classes as well as improving its recognition capabilities as new instances of already known classes are discovered. Ideally, continual learning should be triggered by the availability of short videos of single objects and performed online on onboard hardware. In this paper, we introduce a novel fine-grained continual learning protocol based on the CORe50 benchmark and propose two continual learning techniques that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches.

Connected through hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning"):
  with members:
    - "learning to continually learn"
    - "continuous meta learning without tasks"
    - "reinforcement learning in non stationary environments"
    - "online learned continual compression with stacked quantization modules"
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "regularization shortcomings for continual learning"
    - "meta learning representations for continual learning"

Connected through hyperedge HE-2 (induced by source paper "latent replay for real time continual learning"):
  with members:
    - "rilod near real time incremental learning for object detection at the edge"
    - "are we done with object recognition the icub robot s perspective"
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "efficient continual learning in neural networks with embedding regularization"
    - "openloris object a dataset and benchmark towards lifelong object recognition"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "sequential mastery of multiple visual tasks networks naturally learn to learn and forget to forget"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "task free continual learning"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "deep generative dual memory network for continual learning"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "a simple neural network module for relational reasoning"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-4 (induced by source paper "contcap a comprehensive framework for continual image captioning"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "incremental classifier learning with generative adversarial networks"
    - "neural baby talk"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "continuous learning in single incremental task scenarios"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "end to end incremental learning"
    - "progressive neural networks"

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.LG(Machine Learning)