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: "bindsnet a machine learning oriented spiking neural networks library in python"
  Abstract: The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared towards machine learning and reinforcement learning. Our software, called \texttt{BindsNET}, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. \texttt{BindsNET} is built on the \texttt{PyTorch} deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the \texttt{BindsNET} framework can be adjusted to utilize other existing computing and hardware backends; e.g., \texttt{TensorFlow} and \texttt{SpiNNaker}. We provide an interface with the OpenAI \texttt{gym} library, allowing for training and evaluation of spiking networks on reinforcement learning environments. We argue that this package facilitates the use of spiking networks for large-scale machine learning problems and show some simple examples by using \texttt{BindsNET} in practice. \blfootnote{\texttt{BindsNET} code is available at \texttt{https://github.com/Hananel-Hazan/bindsnet}. To install the version of the code used for this paper, use \texttt{pip install bindsnet=0.2.1}.}

Connected through hyperedge HE-1 (induced by source paper "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"):
  with members:
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"

Connected through hyperedge HE-2 (induced by source paper "minibatch processing in spiking neural networks"):
  with members:
    - "improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari games"
    - "deep learning in spiking neural networks"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "nengodl combining deep learning and neuromorphic modelling methods"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "revisiting small batch training for deep neural networks"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-3 (induced by source paper "locally connected spiking neural networks for unsupervised feature learning"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp learning of image patches with convolutional spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "unsupervised learning with self organizing spiking neural networks"
    - "quantifying translation invariance in convolutional neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "efficient computation in adaptive artificial spiking neural networks"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"

Connected through hyperedge HE-4 (induced by source paper "improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari games"):
  with members:
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep reinforcement learning with double q learning"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-5 (induced by source paper "lattice map spiking neural networks lm snns for clustering and classifying image data"):
  with members:
    - "stdp learning of image patches with convolutional spiking neural networks"
    - "unsupervised learning with self organizing spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "training deep spiking neural networks using backpropagation"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (induced by source paper "faster and simpler snn simulation with work queues"):
  with members:
    - "deep learning in spiking neural networks"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "pipedream fast and efficient pipeline parallel dnn training"

Connected through hyperedge HE-7 (induced by source paper "direct training for spiking neural networks faster larger better"):
  with members:
    - "deep learning in spiking neural networks"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

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)