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 "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.

Includes members:
  Member "overcoming catastrophic forgetting with unlabeled data in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "remind your neural network to prevent catastrophic forgetting")
  Member "learning to remember a synaptic plasticity driven framework for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "learning without memorizing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "continual universal object detection")
  Member "lifelong learning of spatiotemporal representations with dual memory recurrent self organization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "human action recognition and assessment via deep neural network self organization")
  Member "revisiting distillation and incremental classifier learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scail classifier weights scaling for class incremental learning")
  Member "efficient lifelong learning with a gem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "memory replay gans learning to generate images from new categories without forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "continual universal object detection")
  Member "riemannian walk for incremental learning understanding forgetting and intransigence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")

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.LG(Machine Learning)