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: "lutnet rethinking inference in fpga soft logic"
  Abstract: Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised down to binary values. Network binarisation on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the K-LUT, is capable of implementing far more than an XNOR: it can perform any K-input Boolean operation. Inspired by this observation, we propose LUTNet, an end-to-end hardware-software framework for the construction of area-efficient FPGA-based neural network accelerators using the native LUTs as inference operators. We demonstrate that the exploitation of LUT flexibility allows for far heavier pruning than possible in prior works, resulting in significant area savings while achieving comparable accuracy. Against the state-of-the-art binarised neural network implementation, we achieve twice the area efficiency for several standard network models when inferencing popular datasets. We also demonstrate that even greater energy efficiency improvements are obtainable.

Connected through hyperedge HE-1 (induced by source paper "cat compression aware training for bandwidth reduction"):
  with members:
    - "ebpc extended bit plane compression for deep neural network inference and training accelerators"
    - "compressibility loss for neural network weights"
    - "feature map transform coding for energy efficient cnn inference"
    - "quantization networks"
    - "sparse networks from scratch faster training without losing performance"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "efficient and effective quantization for sparse dnns"
    - "focused quantization for sparse cnns"

Connected through hyperedge HE-2 (induced by source paper "feature map transform coding for energy efficient cnn inference"):
  with members:
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "regularizing activation distribution for training binarized deep networks"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "swalp stochastic weight averaging in low precision training"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "uniq uniform noise injection for the quantization of neural networks"

Connected through hyperedge HE-3 (induced by source paper "lutnet learning fpga configurations for highly efficient neural network inference"):
  with members:
    - "focused quantization for sparse cnns"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "in datacenter performance analysis of a tensor processing unit"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rebnet residual binarized neural network"
    - "finn a framework for fast scalable binarized neural network inference"

Connected through hyperedge HE-4 (induced by source paper "rethinking arithmetic for deep neural networks"):
  with members:
    - "stochastic rounding and reduced precision fixed point arithmetic for solving neural odes"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "rebnet residual binarized neural network"
    - "finn a framework for fast scalable binarized neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "neural architecture search with reinforcement learning"

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)