human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "tim dnn ternary in memory accelerator for deep neural networks"):
  Abstract: The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs). In the quest for lower precision, recent studies have shown that ternary DNNs, which represent weights and activations by signed ternary values, represent a promising sweet spot, and achieve accuracy close to full-precision networks on complex tasks such as language modeling and image classification. We propose TiM-DNN, a programmable, in-memory accelerator that is specifically designed to execute ternary DNNs. TiM-DNN supports various ternary representations including unweighted (-1,0,1), symmetric weighted (-a,0,a), and asymmetric weighted (-a,0,b) ternary systems. TiM-DNN is designed using TiM tiles -- specialized memory arrays that perform massively parallel signed vector-matrix multiplications on ternary values with a single access. TiM tiles are in turn composed of Ternary Processing Cells (TPCs), new bit-cells that function as both ternary storage units and signed scalar multiplication units. We evaluate an implementation of TiM-DNN in 32nm technology using an architectural simulator calibrated with SPICE simulations and RTL synthesis. TiM-DNN achieves a peak performance of 114 TOPs/s, consumes 0.9W power, and occupies 1.96mm2 chip area, representing a 300X and 388X improvement in TOPS/W and TOPS/mm2, respectively, compared to a state-of-the-art NVIDIA Tesla V100 GPU. In comparison to popular DNN accelerators, TiM-DNN achieves 55.2X-240X and 160X-291X improvement in TOPS/W and TOPS/mm2, respectively. We compare TiM-DNN with a well-optimized near-memory accelerator for ternary DNNs across a suite of state-of-the-art DNN benchmarks including both deep convolutional and recurrent neural networks, demonstrating 3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup.

Includes members:
  Member "puma a programmable ultra efficient memristor based accelerator for machine learning inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compiling neural networks for a computational memory accelerator")
  Member "rxnn a framework for evaluating deep neural networks on resistive crossbars":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation")
  Member "pact parameterized clipping activation for quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "wrpn wide reduced precision networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "least squares binary quantization of neural networks")
  Member "ternary neural networks with fine grained quantization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")
  Member "computing in memory with spin transfer torque magnetic ram":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a new mram based process in memory accelerator for efficient neural network training with floating point precision")
  Member "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "effective quantization methods for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")

Question: Please tell me which class the source paper 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)