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 "controlled forgetting targeted stimulation and dopaminergic plasticity modulation for unsupervised lifelong learning in spiking neural networks"):
  Abstract: Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data. For a deployed system that must learn online from an uncontrolled and unknown environment, the ordering of input samples often fails to meet this criterion, making lifelong learning a difficult challenge. We exploit the locality of the unsupervised Spike Timing Dependent Plasticity (STDP) learning rule to target local representations in a Spiking Neural Network (SNN) to adapt to novel information while protecting essential information in the remainder of the SNN from catastrophic forgetting. In our Controlled Forgetting Networks (CFNs), novel information triggers stimulated firing and heterogeneously modulated plasticity, inspired by biological dopamine signals, to cause rapid and isolated adaptation in the synapses of neurons associated with outlier information. This targeting controls the forgetting process in a way that reduces the degradation of accuracy for older tasks while learning new tasks. Our experimental results on the MNIST dataset validate the capability of CFNs to learn successfully over time from an unknown, changing environment, achieving 95.36% accuracy, which we believe is the best unsupervised accuracy ever achieved by a fixed-size, single-layer SNN on a completely disjoint MNIST dataset.

Includes members:
  Member "closed loop memory gan for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grim repr prioritising generating important features for pseudo rehearsal")
  Member "continual learning with self organizing maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overcoming catastrophic interference in online reinforcement learning with dynamic self organizing maps")
  Member "memory aware synapses learning what not to forget":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "progressive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "asp learning to forget with adaptive synaptic plasticity in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spiking inception module for multi layer unsupervised spiking neural networks")
  Member "overcoming catastrophic forgetting by incremental moment matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ternary feature masks continual learning without any forgetting")
  Member "pathnet evolution channels gradient descent in super neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "overcoming catastrophic forgetting in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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.NE(Neural and Evolutionary Computing)