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: "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
  Abstract: A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive Synaptic Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for real time on-line learning in a dynamic environment. We incorporate an adaptive weight decay mechanism with the traditional Spike Timing Dependent Plasticity (STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic weights is modulated based on the temporal correlation between the spiking patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual forgetting of insignificant data while retaining significant, yet old, information. ASP, thus, maintains a balance between forgetting and immediate learning to construct a stable-plastic self-adaptive SNN for continuously changing inputs. We demonstrate that the proposed learning methodology addresses catastrophic forgetting while yielding significantly improved accuracy over the conventional STDP learning method for digit recognition applications. Additionally, we observe that the proposed learning model automatically encodes selective attention towards relevant features in the input data while eliminating the influence of background noise (or denoising) further improving the robustness of the ASP learning.

Connected through hyperedge HE-1 (induced by source paper "spiking inception module for multi layer unsupervised spiking neural networks"):
  with members:
    - "biosnet a fast learning and high robustness unsupervised biomimetic spiking neural network"
    - "locally connected spiking neural networks for unsupervised feature learning"
    - "deep learning in spiking neural networks"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "unsupervised learning with self organizing spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "biosnet a fast learning and high robustness unsupervised biomimetic spiking neural network"):
  with members:
    - "locally connected spiking neural networks for unsupervised feature learning"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "emnist an extension of mnist to handwritten letters"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "surrogate gradient learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"

Connected through hyperedge HE-4 (induced by source paper "controlled forgetting targeted stimulation and dopaminergic plasticity modulation for unsupervised lifelong learning in spiking neural networks"):
  with members:
    - "closed loop memory gan for continual learning"
    - "continual learning with self organizing maps"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning without forgetting"

Connected through hyperedge HE-5 (induced by source paper "stimulating stdp to exploit locality for lifelong learning without catastrophic forgetting"):
  with members:
    - "progressive neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning without forgetting"

Connected through hyperedge HE-6 (induced by source paper "incremental learning in deep convolutional neural networks using partial network sharing"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "progressive neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "icarl incremental classifier and representation learning"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-7 (induced by source paper "voltage driven domain wall motion based neuro synaptic devices for dynamic on line learning"):
  with members:
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"
    - "spin orbit torque induced spike timing dependent plasticity"

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.NE(Neural and Evolutionary Computing)