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: "ucnn exploiting computational reuse in deep neural networks via weight repetition"
  Abstract: Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic society (from voice recognition to scene generation) due to their high accuracy and machine efficiency per operation. At their core, CNN computations are made up of multi-dimensional dot products between weight and input vectors. This paper studies how weight repetition ---when the same weight occurs multiple times in or across weight vectors--- can be exploited to save energy and improve performance during CNN inference. This generalizes a popular line of work to improve efficiency from CNN weight sparsity, as reducing computation due to repeated zero weights is a special case of reducing computation due to repeated weights. #R##N#To exploit weight repetition, this paper proposes a new CNN accelerator called the Unique Weight CNN Accelerator (UCNN). UCNN uses weight repetition to reuse CNN sub-computations (e.g., dot products) and to reduce CNN model size when stored in off-chip DRAM ---both of which save energy. UCNN further improves performance by exploiting sparsity in weights. We evaluate UCNN with an accelerator-level cycle and energy model and with an RTL implementation of the UCNN processing element. On three contemporary CNNs, UCNN improves throughput-normalized energy consumption by 1.2x - 4x, relative to a similarly provisioned baseline accelerator that uses Eyeriss-style sparsity optimizations. At the same time, the UCNN processing element adds only 17-24% area overhead relative to the same baseline.

Connected through hyperedge HE-1 (induced by source paper "non structured dnn weight pruning is it beneficial in any platform"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "adversarial robustness vs model compression or both"
    - "hypar towards hybrid parallelism for deep learning accelerator array"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "morph flexible acceleration for 3d cnn based video understanding"

Connected through hyperedge HE-2 (induced by source paper "vargnet variable group convolutional neural network for efficient embedded computing"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "panoptic feature pyramid networks"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tvm end to end optimization stack for deep learning"

Connected through hyperedge HE-3 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference"):
  with members:
    - "software defined design space exploration for an efficient ai accelerator architecture"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image classification at supercomputer scale"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "progressive neural architecture search"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-4 (induced by source paper "a pre defined sparse kernel based convolution for deep cnns"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-5 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "red a reram based deconvolution accelerator"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "pt spike a precise time dependent single spike neuromorphic architecture with efficient supervised learning"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "revisiting small batch training for deep neural networks"
    - "unifying data model and hybrid parallelism in deep learning via tensor tiling"
    - "group scissor scaling neuromorphic computing design to large neural networks"

Connected through hyperedge HE-6 (induced by source paper "mixed signal charge domain acceleration of deep neural networks through interleaved bit partitioned arithmetic"):
  with members:
    - "development of a sensory neural network for medical diagnosing"
    - "yolov3 an incremental improvement"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "low latency neural speech translation"
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"
    - "safedrive enhancing lane appearance for autonomous and assisted driving under limited visibility"
    - "caterpillar coarse grain reconfigurable architecture for accelerating the training of deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "software defined design space exploration for an efficient ai accelerator architecture"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "rethinking atrous convolution for semantic image segmentation"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "wide deep learning for recommender systems"
    - "eie efficient inference engine on compressed deep neural network"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-8 (induced by source paper "recnmp accelerating personalized recommendation with near memory processing"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "bandana using non volatile memory for storing deep learning models"
    - "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional 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.NE(Neural and Evolutionary Computing)