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: "fpga based cnn inference accelerator synthesized from multi threaded c software"
  Abstract: A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. The software implementation uses the well-known producer/consumer model with parallel threads interconnected by FIFO queues. The LegUp high-level synthesis (HLS) [1] tool synthesizes threads into parallel FPGA hardware, translating software parallelism into spatial parallelism. A complete system is generated where convolution, pooling and padding are realized in the synthesized accelerator, with remaining tasks executing on an embedded ARM processor. The accelerator incorporates reduced precision, and a novel approach for zero-weight-skipping in convolution. On a mid-sized Intel Arria 10 SoC FPGA, peak performance on VGG-16 is 138 effective GOPS.

Connected through hyperedge HE-1 (induced by source paper "synergy an hw sw framework for high throughput cnns on embedded heterogeneous soc"):
  with members:
    - "finn a framework for fast scalable binarized neural network inference"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "understanding the impact of precision quantization on the accuracy and energy of neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "tma tera macs w neural hardware inference accelerator with a multiplier less massive parallel processor"):
  with members:
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "module per object a human driven methodology for c based high level synthesis design"):
  with members:
    - "p4 compatible high level synthesis of low latency 100 gb s streaming packet parsers in fpgas"
    - "using vivado hls for structural design a noc case study"
    - "enabling fpgas for the masses"

Connected through hyperedge HE-4 (induced by source paper "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"):
  with members:
    - "approximate fpga based lstms under computation time constraints"
    - "in datacenter performance analysis of a tensor processing unit"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "fpgaconvnet a toolflow for mapping diverse convolutional neural networks on embedded fpgas"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "learning to fly by crashing"
    - "tactics to directly map cnn graphs on embedded fpgas"

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)