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: "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
  Abstract: Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks. However, the recent trend to design Deep Neural Networks (DNNs) for achieving human-like cognitive abilities poses significant challenges towards the scalable design of neuromorphic systems (due to the increase in computation/storage demands). Network pruning [7] is a powerful technique to remove redundant connections for designing optimally connected (maximally sparse) DNNs. However, such pruning techniques induce irregular connections that are incoherent to the crossbar structure. Eventually they produce DNNs with highly inefficient hardware realizations (in terms of area and energy). In this work, we propose TraNNsformer - an integrated training framework that transforms DNNs to enable their efficient realization on MCA-based systems. TraNNsformer first prunes the connectivity matrix while forming clusters with the remaining connections. Subsequently, it retrains the network to fine tune the connections and reinforce the clusters. This is done iteratively to transform the original connectivity into an optimally pruned and maximally clustered mapping. We evaluated the proposed framework by transforming different Multi-Layer Perceptron (MLP) based Spiking Neural Networks (SNNs) on a wide range of datasets (MNIST, SVHN and CIFAR10) and executing them on MCA-based systems to analyze the area and energy benefits. Without accuracy loss, TraNNsformer reduces the area (energy) consumption by 28% - 55% (49% - 67%) with respect to the original network. Compared to network pruning, TraNNsformer achieves 28% - 49% (15% - 29%) area (energy) savings. Furthermore, TraNNsformer is a technology-aware framework that allows mapping a given DNN to any MCA size permissible by the memristive technology for reliable operations.

Connected through hyperedge HE-1 (induced by source paper "structured compression and sharing of representational space for continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "gradient based sample selection for online continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "a low effort approach to structured cnn design using pca"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "generative replay with feedback connections as a general strategy for continual learning"

Connected through hyperedge HE-2 (induced by source paper "puma a programmable ultra efficient memristor based accelerator for machine learning inference"):
  with members:
    - "vibnn hardware acceleration of bayesian neural networks"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bridging the gap between neural networks and neuromorphic hardware with a neural network compiler"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"

Connected through hyperedge HE-3 (induced by source paper "an ultra efficient memristor based dnn framework with structured weight pruning and quantization using admm"):
  with members:
    - "26ms inference time for resnet 50 towards real time execution of all dnns on smartphone"
    - "toward extremely low bit and lossless accuracy in dnns with progressive admm"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "resnet can be pruned 60x introducing network purification and unused path removal p rm after weight pruning"
    - "an area and energy efficient design of domain wall memory based deep convolutional neural networks using stochastic computing"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "scalable noc based neuromorphic hardware learning and inference"
    - "group scissor scaling neuromorphic computing design to large neural networks"

Connected through hyperedge HE-4 (induced by source paper "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "neuromorphic computing with multi memristive synapses"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"

Connected through hyperedge HE-5 (induced by source paper "pabo pseudo agent based multi objective bayesian hyperparameter optimization for efficient neural accelerator design"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "dvolver efficient pareto optimal neural network architecture search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "neural networks designing neural networks multi objective hyper parameter optimization"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "crossbar aware neural network pruning"):
  with members:
    - "bridging the gap between neural networks and neuromorphic hardware with a neural network compiler"
    - "mec memory efficient convolution for deep neural network"
    - "learning intrinsic sparse structures within long short term memory"
    - "thinet a filter level pruning method for deep neural network compression"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep 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.ET(Emerging Technologies)