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: "mec memory efficient convolution for deep neural network"
  Abstract: Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor performance. As an alternative, multiple indirect methods have been proposed including im2col-based convolution, FFT-based convolution, or Winograd-based algorithm. However, all these indirect methods have high memory-overhead, which creates performance degradation and offers a poor trade-off between performance and memory consumption. In this work, we propose a memory-efficient convolution or MEC with compact lowering, which reduces memory-overhead substantially and accelerates convolution process. MEC lowers the input matrix in a simple yet efficient/compact way (i.e., much less memory-overhead), and then executes multiple small matrix multiplications in parallel to get convolution completed. Additionally, the reduced memory footprint improves memory sub-system efficiency, improving performance. Our experimental results show that MEC reduces memory consumption significantly with good speedup on both mobile and server platforms, compared with other indirect convolution algorithms.

Connected through hyperedge HE-1 (induced by source paper "high performance depthwise and pointwise convolutions on mobile devices"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "diagonalwise refactorization an efficient training method for depthwise convolutions"
    - "high performance zero memory overhead direct convolutions"
    - "mnasnet platform aware neural architecture search for mobile"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-2 (induced by source paper "the indirect convolution algorithm"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "panoptic feature pyramid networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "high performance zero memory overhead direct convolutions"
    - "pooling pyramid network for object detection"

Connected through hyperedge HE-3 (induced by source paper "cheetah mixed low precision hardware software co design framework for dnns on the edge"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-4 (induced by source paper "deep learning training on the edge with low precision posits"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "template based posit multiplication for training and inferring in neural networks"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-5 (induced by source paper "learning low rank approximation for cnns"):
  with members:
    - "model compression via distillation and quantization"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "retraining based iterative weight quantization for deep neural networks"
    - "deeptwist learning model compression via occasional weight distortion"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "in datacenter performance analysis of a tensor processing unit"
    - "tensorly tensor learning in python"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-6 (induced by source paper "fusionaccel a general re configurable deep learning inference accelerator on fpga for convolutional neural networks"):
  with members:
    - "squeezenext hardware aware neural network design"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "hardware oriented approximation of convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-7 (induced by source paper "recent advances in convolutional neural network acceleration"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
    - "coordinating filters for faster deep neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-8 (induced by source paper "crossbar aware neural network pruning"):
  with members:
    - "bridging the gap between neural networks and neuromorphic hardware with a neural network compiler"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "learning intrinsic sparse structures within long short term memory"
    - "thinet a filter level pruning method for deep neural network compression"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"

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)