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: "precise deep neural network computation on imprecise low power analog hardware"
  Abstract: There is an urgent need for compact, fast, and power-efficient hardware implementations of state-of-the-art artificial intelligence. Here we propose a power-efficient approach for real-time inference, in which deep neural networks (DNNs) are implemented through low-power analog circuits. Although analog implementations can be extremely compact, they have been largely supplanted by digital designs, partly because of device mismatch effects due to fabrication. We propose a framework that exploits the power of Deep Learning to compensate for this mismatch by incorporating the measured variations of the devices as constraints in the DNN training process. This eliminates the use of mismatch minimization strategies such as the use of very large transistors, and allows circuit complexity and power-consumption to be reduced to a minimum. Our results, based on large-scale simulations as well as a prototype VLSI chip implementation indicate at least a 3-fold improvement of processing efficiency over current digital implementations.

Connected through hyperedge HE-1 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "all optical machine learning using diffractive deep neural networks"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-2 (induced by source paper "adaptive learning rule for hardware based deep neural networks using electronic synapse devices"):
  with members:
    - "lstm a search space odyssey"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "deep residual learning for image recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "redesigning commercial floating gate memory for analog computing applications"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "adaptive motor control and learning in a spiking neural network realised on a mixed signal neuromorphic processor"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "learning and inferring relations in cortical networks"
    - "kickstarting high performance energy efficient manycore architectures with epiphany"

Connected through hyperedge HE-4 (induced by source paper "artificial retina using a hybrid neural network with spatial transform capability"):
  with members:
    - "memory and information processing in neuromorphic systems"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-5 (induced by source paper "energy efficient time domain vector by matrix multiplier for neurocomputing and beyond"):
  with members:
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "in datacenter performance analysis of a tensor processing unit"
    - "temperature insensitive analog vector by matrix multiplier based on 55 nm nor flash memory cells"
    - "sub 1 us sub 20 nj pattern classification in a mixed signal circuit based on embedded 180 nm floating gate memory cell arrays"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "jpeg image compression using discrete cosine transform a survey"

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)