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 "single net continual learning with progressive segmented training pst"):
  Abstract: There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference. Different from previous approaches with dynamic structures, this work focuses on a single network and model segmentation to prevent catastrophic forgetting. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and secondary group to be saved (not pruned) for a future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of PST successfully incorporates multiple tasks and achieves the state-of-the-art accuracy in the single-head evaluation on CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning.

Includes members:
  Member "class incremental learning via deep model consolidation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ternary feature masks continual learning without any forgetting")
  Member "efficient network construction through structural plasticity":
    also connected through other hyperedges:
      (none)
  Member "automatic compiler based fpga accelerator for cnn training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms")
  Member "regularize expand and compress multi task based lifelong learning via nonexpansive automl":
    also connected through other hyperedges:
      (none)
  Member "large scale incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "towards efficient neural networks on a chip joint hardware algorithm approaches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structural pruning in deep neural networks a small world approach")
  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 "reinforced continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)