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: "caffe con troll shallow ideas to speed up deep learning"
  Abstract: We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural networks across different hardware architectures. We find that, by employing standard batching optimizations for CPU training, we achieve a 4.5x throughput improvement over Caffe on popular networks like CaffeNet. Moreover, with these improvements, the end-to-end training time for CNNs is directly proportional to the FLOPS delivered by the CPU, which enables us to efficiently train hybrid CPU-GPU systems for CNNs.

Connected through hyperedge HE-1 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "bigdl a distributed deep learning framework for big data"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-2 (induced by source paper "gemmini an agile systolic array generator enabling systematic evaluations of deep learning architectures"):
  with members:
    - "integrating nvidia deep learning accelerator nvdla with risc v soc on firesim"
    - "fpsa a full system stack solution for reconfigurable reram based nn accelerator architecture"
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "dnn dataflow choice is overrated"
    - "vta an open hardware software stack for deep learning"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-3 (induced by source paper "escoin efficient sparse convolutional neural network inference on gpus"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "gardenia a domain specific benchmark suite for next generation accelerators"
    - "enabling sparse winograd convolution by native pruning"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "learning structured sparsity in deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "eie efficient inference engine on compressed deep neural network"
    - "faster cnns with direct sparse convolutions and guided pruning"

Connected through hyperedge HE-4 (induced by source paper "deep learning approximation zero shot neural network speedup"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "yolo9000 better faster stronger"
    - "factorized convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-5 (induced by source paper "cuttlefish a lightweight primitive for adaptive query processing"):
  with members:
    - "distributed clustering of linear bandits in peer to peer networks"
    - "on context dependent clustering of bandits"
    - "comparative evaluation of big data systems on scientific image analytics workloads"
    - "mllib machine learning in apache spark"
    - "show attend and tell neural image caption generation with visual attention"
    - "on the prior sensitivity of thompson sampling"
    - "rethinking the inception architecture for computer vision"
    - "online clustering of bandits"

Connected through hyperedge HE-6 (induced by source paper "stochastic gradient descent on highly parallel architectures"):
  with members:
    - "a cost based optimizer for gradient descent optimization"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "database meets deep learning challenges and opportunities"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "dimmwitted a study of main memory statistical analytics"
    - "the madlib analytics library or mad skills the sql"

Connected through hyperedge HE-7 (induced by source paper "optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption"):
  with members:
    - "imagenet training in minutes"
    - "billion scale similarity search with gpus"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"
    - "a survey and measurement study of gpu dvfs on energy conservation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "energy aware task partitioning on heterogeneous multiprocessor platforms"
    - "gridsim a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing"

Connected through hyperedge HE-8 (induced by source paper "distributed training large scale deep architectures"):
  with members:
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "comparative study of deep learning software frameworks"
    - "benchmarking state of the art deep learning software tools"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "poseidon a system architecture for efficient gpu based deep learning on multiple machines"

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)