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: "lifelong machine learning with deep streaming linear discriminant analysis"
  Abstract: When a robot acquires new information, ideally it would immediately be capable of using that information to understand its environment. While deep neural networks are now widely used by robots for inferring semantic information, conventional neural networks suffer from catastrophic forgetting when they are incrementally updated, with new knowledge overwriting established representations. While a variety of approaches have been developed that attempt to mitigate catastrophic forgetting in the incremental batch learning scenario, in which an agent learns a large collection of labeled samples at once, streaming learning has been much less studied in the robotics and deep learning communities. In streaming learning, an agent learns instances one-by-one and can be tested at any time. Here, we revisit streaming linear discriminant analysis, which has been widely used in the data mining research community. By combining streaming linear discriminant analysis with deep learning, we are able to outperform both incremental batch learning and streaming learning algorithms on both ImageNet-1K and CORe50.

Connected through hyperedge HE-1 (induced by source paper "remind your neural network to prevent catastrophic forgetting"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "large scale incremental learning"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "answer them all toward universal visual question answering models"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "bilinear attention networks"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"

Connected through hyperedge HE-2 (induced by source paper "unsupervised progressive learning and the stam architecture"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "continual learning via neural pruning"
    - "three scenarios for continual learning"
    - "continual learning with tiny episodic memories"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-3 (induced by source paper "fine grained continual learning"):
  with members:
    - "are we done with object recognition the icub robot s perspective"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the limits and potentials of deep learning for robotics"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "continuous learning in single incremental task scenarios"

Connected through hyperedge HE-4 (induced by source paper "latent replay for real time continual learning"):
  with members:
    - "fine grained continual learning"
    - "rilod near real time incremental learning for object detection at the edge"
    - "are we done with object recognition the icub robot s perspective"
    - "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"

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)