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: "deep learning on fpgas past present and future"
  Abstract: The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in important domains such as computer vision, speech recognition, and natural language processing. The most popular class of techniques used in these domains is called deep learning, and is seeing significant attention from industry. However, these models require incredible amounts of data and compute power to train, and are limited by the need for better hardware acceleration to accommodate scaling beyond current data and model sizes. While the current solution has been to use clusters of graphics processing units (GPU) as general purpose processors (GPGPU), the use of field programmable gate arrays (FPGA) provide an interesting alternative. Current trends in design tools for FPGAs have made them more compatible with the high-level software practices typically practiced in the deep learning community, making FPGAs more accessible to those who build and deploy models. Since FPGA architectures are flexible, this could also allow researchers the ability to explore model-level optimizations beyond what is possible on fixed architectures such as GPUs. As well, FPGAs tend to provide high performance per watt of power consumption, which is of particular importance for application scientists interested in large scale server-based deployment or resource-limited embedded applications. This review takes a look at deep learning and FPGAs from a hardware acceleration perspective, identifying trends and innovations that make these technologies a natural fit, and motivates a discussion on how FPGAs may best serve the needs of the deep learning community moving forward.

Connected through hyperedge HE-1 (induced by source paper "poet bin power efficient tiny binary neurons"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "towards effective low bitwidth convolutional neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "text understanding with the attention sum reader network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "from facial parts responses to face detection a deep learning approach"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "fpga based accelerators of deep learning networks for learning and classification a review"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "deep learning for sentiment analysis a survey"
    - "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"
    - "hardware for machine learning challenges and opportunities"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "parallel multi channel convolution using general matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "progressive stochastic binarization of deep networks"):
  with members:
    - "deep convolutional sum product networks for probabilistic image representations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "probabilistic binary neural networks"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "learning to train a binary neural network"
    - "bnn improved binary network training"

Connected through hyperedge HE-4 (induced by source paper "big data analytics for large scale wireless networks challenges and opportunities"):
  with members:
    - "blockchain for internet of things a survey"
    - "big data analytics for manufacturing internet of things opportunities challenges and enabling technologies"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "stealing hyperparameters in machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"

Connected through hyperedge HE-5 (induced by source paper "deep learning on mobile devices a review"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the limits and potentials of deep learning for robotics"
    - "ai benchmark running deep neural networks on android smartphones"
    - "transfer learning for time series classification"
    - "an analysis of deep neural network models for practical applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "toward robustness against label noise in training deep discriminative neural networks"

Connected through hyperedge HE-6 (induced by source paper "2l 3w 2 level 3 way hardware software co verification for the mapping of deep learning architecture dla onto fpga boards"):
  with members:
    - "detecting sybil attacks using proofs of work and location in vanets"
    - "privacy preserving smart parking system using blockchain and private information retrieval"
    - "b ride ride sharing with privacy preservation trust and fair payment atop public blockchain"
    - "privacy preserving and collusion resistant charging coordination schemes for smart grid"
    - "on sharing models instead of data using mimic learning for smart health applications"
    - "mimic learning to generate a shareable network intrusion detection model"
    - "a stealthy hardware trojan exploiting the architectural vulnerability of deep learning architectures input interception attack iia"
    - "a scalable multilabel classification to deploy deep learning architectures for edge devices"

Connected through hyperedge HE-7 (induced by source paper "efficient hardware implementation of incremental learning and inference on chip"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "constructing fast network through deconstruction of convolution"
    - "transfer incremental learning using data augmentation"
    - "shift a zero flop zero parameter alternative to spatial convolutions"
    - "towards the limit of network quantization"
    - "icarl incremental classifier and representation learning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "quantized convolutional neural networks for mobile devices"

Connected through hyperedge HE-8 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "bigdl a distributed deep learning framework for big data"
    - "improving data quality through deep learning and statistical models"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "chainermn scalable distributed deep learning framework"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "blocks and fuel frameworks for deep learning"

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.DC(Distributed, Parallel, and Cluster Computing)