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: "a self driving robot using deep convolutional neural networks on neuromorphic hardware"
  Abstract: Neuromorphic computing is a promising solution for reducing the size, weight and power of mobile embedded systems. In this paper, we introduce a realization of such a system by creating the first closed-loop battery-powered communication system between an IBM TrueNorth NS1e and an autonomous Android-Based Robotics platform. Using this system, we constructed a dataset of path following behavior by manually driving the Android-Based robot along steep mountain trails and recording video frames from the camera mounted on the robot along with the corresponding motor commands. We used this dataset to train a deep convolutional neural network implemented on the TrueNorth NS1e. The NS1e, which was mounted on the robot and powered by the robot's battery, resulted in a self-driving robot that could successfully traverse a steep mountain path in real time. To our knowledge, this represents the first time the TrueNorth NS1e neuromorphic chip has been embedded on a mobile platform under closed-loop control.

Connected through hyperedge HE-1 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient"):
  with members:
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "snn under attack are spiking deep belief networks vulnerable to adversarial examples"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "adversarial patch"
    - "direct training for spiking neural networks faster larger better"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-2 (induced by source paper "spiking neural predictive coding for continual learning from data streams"):
  with members:
    - "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule"
    - "unsupervised learning by competing hidden units"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "like a baby visually situated neural language acquisition"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "tampernn efficient tampering detection of deployed neural nets"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "digital watermarking for deep neural networks"
    - "have you stolen my model evasion attacks against deep neural network watermarking techniques"
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "magnet a two pronged defense against adversarial examples"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "neuromodulated patience for robot and self driving vehicle navigation"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "spiking neural network on neuromorphic hardware for energy efficient unidimensional slam"):
  with members:
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal backpropagation for training high performance spiking neural networks"):
  with members:
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "spiking deep networks with lif neurons"
    - "an objective function for stdp"
    - "gated feedback recurrent neural networks"
    - "event driven contrastive divergence for spiking neuromorphic systems"

Connected through hyperedge HE-7 (induced by source paper "cellular automata applications in shortest path problem"):
  with members:
    - "towards implementation of cellular automata in microbial fuel cells"

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)