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: "deep neural network approximation for custom hardware where we ve been where we re going"
  Abstract: Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, however, and so methods for hardware-oriented approximation have become a hot topic. Research has shown that custom hardware-based neural network accelerators can surpass their general-purpose processor equivalents in terms of both throughput and energy efficiency. Application-tailored accelerators, when co-designed with approximation-based network training methods, transform large, dense, and computationally expensive networks into small, sparse, and hardware-efficient alternatives, increasing the feasibility of network deployment. In this article, we provide a comprehensive evaluation of approximation methods for high-performance network inference along with in-depth discussion of their effectiveness for custom hardware implementation. We also include proposals for future research based on a thorough analysis of current trends. This article represents the first survey providing detailed comparisons of custom hardware accelerators featuring approximation for both convolutional and recurrent neural networks, through which we hope to inspire exciting new developments in the field.

Connected through hyperedge HE-1 (induced by source paper "taurus an intelligent data plane"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "meta learning neural bloom filters"
    - "fpga based accelerators of deep learning networks for learning and classification a review"
    - "experience driven networking a deep reinforcement learning based approach"
    - "in network neural networks"
    - "structured control nets for deep reinforcement learning"
    - "training deep neural networks with 8 bit floating point numbers"
    - "how many mice make an elephant modelling flow length and size distribution of internet traffic"

Connected through hyperedge HE-2 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"

Connected through hyperedge HE-3 (induced by source paper "codenet training large scale neural networks in presence of soft errors"):
  with members:
    - "accumulation bit width scaling for ultra low precision training of deep networks"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "training deep neural networks with 8 bit floating point numbers"
    - "fundamental limits of coded linear transform"

Connected through hyperedge HE-4 (induced by source paper "lutnet learning fpga configurations for highly efficient neural network inference"):
  with members:
    - "lutnet rethinking inference in fpga soft logic"
    - "focused quantization for sparse cnns"
    - "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-5 (induced by source paper "lutnet rethinking inference in fpga soft logic"):
  with members:
    - "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"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"

Connected through hyperedge HE-6 (induced by source paper "deepcabac context adaptive binary arithmetic coding for deep neural network compression"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "entropy constrained training of deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "critical learning periods in deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-7 (induced by source paper "rethinking arithmetic for deep neural networks"):
  with members:
    - "lutnet rethinking inference in fpga soft logic"
    - "stochastic rounding and reduced precision fixed point arithmetic for solving neural odes"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "automatic compiler based fpga accelerator for cnn training"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "revisiting small batch training for deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "squeeze and excitation networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "deep learning with limited numerical precision"

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.CV(Computer Vision and Pattern Recognition)