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: "fpgaconvnet a toolflow for mapping diverse convolutional neural networks on embedded fpgas"
  Abstract: In recent years, Convolutional Neural Networks (ConvNets) have become an enabling technology for a wide range of novel embedded Artificial Intelligence systems. Across the range of applications, the performance needs vary significantly, from high-throughput video surveillance to the very low-latency requirements of autonomous cars. In this context, FPGAs can provide a potential platform that can be optimally configured based on the different performance needs. However, the complexity of ConvNet models keeps increasing making their mapping to an FPGA device a challenging task. This work presents fpgaConvNet, an end-to-end framework for mapping ConvNets on FPGAs. The proposed framework employs an automated design methodology based on the Synchronous Dataflow (SDF) paradigm and defines a set of SDF transformations in order to efficiently explore the architectural design space. By selectively optimising for throughput, latency or multiobjective criteria, the presented tool is able to efficiently explore the design space and generate hardware designs from high-level ConvNet specifications, explicitly optimised for the performance metric of interest. Overall, our framework yields designs that improve the performance by up to 6.65x over highly optimised embedded GPU designs for the same power constraints in embedded environments.

Connected through hyperedge HE-1 (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"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "fpga based cnn inference accelerator synthesized from multi threaded c software"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "learning to fly by crashing"
    - "tactics to directly map cnn graphs on embedded fpgas"

Connected through hyperedge HE-2 (induced by source paper "deploying deep neural networks in the embedded space"):
  with members:
    - "f cnn text x a toolflow for mapping multiple convolutional neural networks on fpgas"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "approximate fpga based lstms under computation time constraints"
    - "dronet efficient convolutional neural network detector for real time uav applications"
    - "cascadecnn pushing the performance limits of quantisation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mask r cnn"
    - "speed accuracy trade offs for modern convolutional object detectors"

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.CV(Computer Vision and Pattern Recognition)