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: "rapidnn in memory deep neural network acceleration framework"
  Abstract: Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-the-art DNNs on current systems mostly relies on either generalpurpose processors, ASIC designs, or FPGA accelerators, all of which suffer from data movements due to the limited onchip memory and data transfer bandwidth. In this work, we propose a novel framework, called RAPIDNN, which processes all DNN operations within the memory to minimize the cost of data movement. To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i.e., multiplication, addition, activation functions, and pooling. The framework extracts representative operands of a DNN model, e.g., weights and input values, using clustering methods to optimize the model for in-memory processing. Then, it maps the extracted operands and their precomputed results into the accelerator memory blocks. At runtime, the accelerator identifies computation results based on efficient in-memory search capability which also provides tunability of approximation to further improve computation efficiency. Our evaluation shows that RAPIDNN achieves 68.4x, 49.5x energy efficiency improvement and 48.1x, 10.9x speedup as compared to ISAAC and PipeLayer, the state-of-the-art DNN accelerators, while ensuring less than 0.3% of quality loss.

Connected through hyperedge HE-1 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "in dram bulk bitwise execution engine"
    - "analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "nullanet training deep neural networks for reduced memory access inference"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-2 (induced by source paper "3d acortex an ultra compact energy efficient neurocomputing platform based on commercial 3d nand flash memories"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "a behavioral compact model for static characteristics of 3d nand flash memory"
    - "a time domain analog weighted sum calculation model for extremely low power vlsi implementation of multi layer neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "deep learning for iot big data and streaming analytics a survey"
    - "energy efficient time domain vector by matrix multiplier for neurocomputing and beyond"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)