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: "multi task zipping via layer wise neuron sharing"
  Abstract: Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks on-device. Yet the complexity of these neural networks needs to be trimmed down both within-model and cross-model to fit in mobile storage and memory. Previous studies focus on squeezing the redundancy within a single neural network. In this work, we aim to reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to automatically merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme that induces a minimal change in the error function. MTZ inherits information from each model and demands light retraining to re-boost the accuracy of individual tasks. Evaluations show that MTZ is able to fully merge the hidden layers of two VGG-16 networks with a 3.18% increase in the test error averaged on ImageNet and CelebA, or share 39.61% parameters between the two networks with <0.5% increase in the test errors for both tasks. The number of iterations to retrain the combined network is at least 17.8 times lower than that of training a single VGG-16 network. Moreover, experiments show that MTZ is also able to effectively merge multiple residual networks.

Connected through hyperedge HE-1 (induced by source paper "feature partitioning for efficient multi task architectures"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "combating catastrophic forgetting with developmental compression"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-2 (induced by source paper "deep elastic networks with model selection for multi task learning"):
  with members:
    - "deep virtual networks for memory efficient inference of multiple tasks"
    - "maskconnect connectivity learning by gradient descent"
    - "a novel channel pruning method for deep neural network compression"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "focal loss for dense object detection"
    - "blockdrop dynamic inference paths in residual networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "customizing student networks from heterogeneous teachers via adaptive knowledge amalgamation"):
  with members:
    - "knowledge amalgamation from heterogeneous networks by common feature learning"
    - "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"
    - "knowledge flow improve upon your teachers"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "few sample knowledge distillation for efficient network compression"
    - "amalgamating knowledge towards comprehensive classification"
    - "single shot refinement neural network for object detection"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-4 (induced by source paper "disentangling redundancy for multi task pruning"):
  with members:
    - "branched multi task networks deciding what layers to share"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "compressing neural networks using the variational information bottleneck"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "estimating mixture entropy with pairwise distances"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

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)