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: "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip"
  Abstract: IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency. However, in TrueNorth chip, low quantization resolution of the synaptic weights and spikes significantly limits the inference (e.g., classification) accuracy of the deployed neural network model. Existing workaround, i.e., averaging the results over multiple copies instantiated in spatial and temporal domains, rapidly exhausts the hardware resources and slows down the computation. In this work, we propose a novel learning method on TrueNorth platform that constrains the random variance of each computation copy and reduces the number of needed copies. Compared to the existing learning method, our method can achieve up to 68.8% reduction of the required neuro-synaptic cores or 6.5× speedup, with even slightly improved inference accuracy.

Connected through hyperedge HE-1 (induced by source paper "vibnn hardware acceleration of bayesian neural networks"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "self normalizing neural networks"
    - "bayesian recurrent neural networks"
    - "hardware oriented approximation of convolutional neural networks"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "eie efficient inference engine on compressed deep neural network"
    - "vime variational information maximizing exploration"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (induced by source paper "deep learning with the random neural network and its applications"):
  with members:
    - "deep learning in multi layer architectures of dense nuclei"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "adaptive dispatching of tasks in the cloud"
    - "striving for simplicity the all convolutional net"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-3 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware"):
  with members:
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "unsupervised learning in neuromemristive systems"
    - "a nonparametric framework for quantifying generative inference on neuromorphic systems"
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-4 (induced by source paper "classification accuracy improvement for neuromorphic computing systems with one level precision synapses"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "group scissor scaling neuromorphic computing design to large neural networks"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "deep residual learning for image recognition"

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)