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: "bit fusion bit level dynamically composable architecture for accelerating deep neural networks"
  Abstract: Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous compute intensity. Fully realizing the potential of acceleration in this domain requires understanding and leveraging algorithmic properties of DNNs. This paper builds upon the algorithmic insight that bitwidth of operations in DNNs can be reduced without compromising their accuracy. However, to prevent accuracy loss, the bitwidth varies significantly across DNNs and it may even be adjusted for each layer individually. Thus, a fixed-bitwidth accelerator would either offer limited benefits to accommodate the worst-case bitwidth, or inevitably lead to a degradation in final accuracy. To alleviate these deficiencies, this work introduces dynamic bit-level fusion/decomposition as a new dimension in the design of DNN accelerators. We explore this dimension by designing Bit Fusion, a bit-flexible accelerator, that constitutes an array of bit-level processing elements that dynamically fuse to match the bitwidth of individual DNN layers. This flexibility in the architecture minimizes the computation and the communication at the finest granularity possible with no loss in accuracy. We evaluate the benefits of Bit Fusion using eight real-world feed-forward and recurrent DNNs. The proposed microarchitecture is implemented in Verilog and synthesized in 45 nm technology. Using the synthesis results and cycle accurate simulation, we compare the benefits of Bit Fusion to two state-of-the-art DNN accelerators, Eyeriss and Stripes. In the same area, frequency, and technology node, Bit Fusion offers 4.3x speedup and 9.6x energy savings over Eyeriss. Bit Fusion provides 2.4x speedup and 4.1x energy reduction over Stripes at 45 nm node when Bit Fusion area and frequency are set to those of Stripes. Compared to Jetson-TX2, Bit Fusion offers 4.3x speedup and almost matches the performance of TitanX, which is 4.6x faster than TX2.

Connected through hyperedge HE-1 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "bandana using non volatile memory for storing deep learning models"
    - "exploring the limits of weakly supervised pretraining"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "rosetta large scale system for text detection and recognition in images"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "precision highway for ultra low precision quantization"):
  with members:
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "high performance ultra low precision convolutions on mobile devices"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "rethinking machine learning development and deployment for edge devices"):
  with members:
    - "quantizing convolutional neural networks for low power high throughput inference engines"
    - "not all ops are created equal"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)