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: "towards efficient neural networks on a chip joint hardware algorithm approaches"
  Abstract: Machine learning algorithms have made significant advances in many applications. However, their hardware implementation on the state-of-the-art platforms still faces several challenges and are limited by various factors, such as memory volume, memory bandwidth and interconnection overhead. The adoption of the crossbar architecture with emerging memory technology partially solves the problem but induces process variation and other concerns. In this paper, we will present novel solutions to two fundamental issues in crossbar implementation of Artificial Intelligence (AI) algorithms: device variation and insufficient interconnections. These solutions are inspired by the statistical properties of algorithms themselves, especially the redundancy in neural network nodes and connections. By Random Sparse Adaptation and pruning the connections following the Small-World model, we demonstrate robust and efficient performance on representative datasets such as MNIST and CIFAR-10. Moreover, we present Continuous Growth and Pruning algorithm for future learning and adaptation on hardware.

Connected through hyperedge HE-1 (induced by source paper "single net continual learning with progressive segmented training pst"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "efficient network construction through structural plasticity"
    - "automatic compiler based fpga accelerator for cnn training"
    - "regularize expand and compress multi task based lifelong learning via nonexpansive automl"
    - "large scale incremental learning"
    - "revisiting distillation and incremental classifier learning"
    - "reinforced continual learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-2 (induced by source paper "structural pruning in deep neural networks a small world approach"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "training sparse neural networks"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "pruning filters for efficient convnets"
    - "tensorflow a system for large scale machine learning"
    - "learning both weights and connections for efficient 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)