human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bridging the gap between neural networks and neuromorphic hardware with a neural network compiler"):
  Abstract: Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions, including the limited precision of network signals and parameters, constrained computation scale and limited types of non-linear functions. This paper proposes a general methodology to address the challenge. It can transform an existing trained, unrestricted NN (usually for software execution substrate) into an equivalent network that meets the given hardware constraints, which decouples NN applications from target hardware. Formally, the original NN is expressed as a computational graph (CG) that would be fine-tuned part by part according to a topological ordering to become the target CG. Quite a few techniques, including the multilayer-perceptron(MLP)-based universal approximator, a data re-encoding method, a split-and-merge network reconstruction method and a multi-phase weight-tuning algorithm, are proposed to conquer the above restrictions respectively. We have built such a software tool that supports both spiking neural networks (SNNs) and traditional artificial neural networks (ANNs). Its effectiveness has been demonstrated with a real neuromorphic chip and a processing-in-memory (PIM) design. Tests show that the extra inference error caused by this solution is very limited and the transformation time is much less than the retraining time. In addition, quite a few parameter-sensitivity evaluations have been completed to explore the tradeoff between network error, resource consumption and different transformation strategies, which could provide insights for co-design optimization of neuromorphic hardware and software.

Includes members:
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "origami a 803 gop s w convolutional network accelerator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "training deep spiking neural networks using backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "training spiking deep networks for neuromorphic hardware":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal spike sequence learning via backpropagation for deep spiking neural networks")
  Member "all you need is a good init":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "deep compression compressing deep neural networks with pruning trained quantization and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)