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: "interleaver design for deep neural networks"
  Abstract: We propose a class of interleavers for a novel deep neural network (DNN) architecture that uses algorithmically predetermined, structured sparsity to significantly lower memory and computational requirements, and speed up training. The interleavers guarantee clash-free memory accesses to eliminate idle operational cycles, optimize spread and dispersion to improve network performance, and are designed to ease the complexity of memory address computations in hardware. We present a design algorithm with mathematical proofs for these properties. We also explore interleaver variations and analyze the behavior of neural networks as a function of interleaver metrics.

Connected through hyperedge HE-1 (induced by source paper "pre defined sparse neural networks with hardware acceleration"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recursive binary neural network learning model with 2 bit weight storage requirement"
    - "morse code datasets for machine learning"
    - "a highly parallel fpga implementation of sparse neural network training"
    - "revisiting small batch training for deep neural networks"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-2 (induced by source paper "characterizing sparse connectivity patterns in neural networks"):
  with members:
    - "accelerating training of deep neural networks via sparse edge processing"
    - "sparse neural networks topologies"
    - "training sparse neural networks"
    - "densely connected convolutional networks"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "morse code datasets for machine learning"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "toward geometric deep slam"
    - "using synthetic data to train neural networks is model based reasoning"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-4 (induced by source paper "a highly parallel fpga implementation of sparse neural network training"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "understanding deep learning requires rethinking generalization"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "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.LG(Machine Learning)