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: "ablation of a robot s brain neural networks under a knife"
  Abstract: It is still not fully understood exactly how neural networks are able to solve the complex tasks that have recently pushed AI research forward. We present a novel method for determining how information is structured inside a neural network. Using ablation (a neuroscience technique for cutting away parts of a brain to determine their function), we approach several neural network architectures from a biological perspective. Through an analysis of this method's results, we examine important similarities between biological and artificial neural networks to search for the implicit knowledge locked away in the network's weights.

Connected through hyperedge HE-1 (induced by source paper "selectivity considered harmful evaluating the causal impact of class selectivity in dnns"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "deep residual learning for image recognition"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-2 (induced by source paper "anonet weakly supervised anomaly detection in textured surfaces"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "understanding neural networks via feature visualization a survey"
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "understanding individual neuron importance using information theory"):
  with members:
    - "distilling critical paths in convolutional neural networks"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "revisiting the importance of individual units in cnns via ablation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "estimating information flow in neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "opening the black box of deep neural networks via information"
    - "on extractable shared information"

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)