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 "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}.}

Includes members:
  Member "unsupervised learning with self organizing spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spiking inception module for multi layer unsupervised spiking neural networks")
  Member "spatio temporal backpropagation for training high performance spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient")
  Member "deep spiking networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning in spiking neural networks")
  Member "supervised learning based on temporal coding in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "training deep spiking neural networks using backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "theano a python framework for fast computation of mathematical expressions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "spiking deep networks with lif neurons":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions")
  Member "towards biologically plausible deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sideways depth parallel training of video models")

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)