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: "approximate fpga based lstms under computation time constraints"
  Abstract: Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks. However, the models are becoming increasingly demanding in terms of computational and memory load. Emerging latency-sensitive applications including mobile robots and autonomous vehicles often operate under stringent computation time constraints. In this paper, we address the challenge of deploying computationally demanding LSTMs at a constrained time budget by introducing an approximate computing scheme that combines iterative low-rank compression and pruning, along with a novel FPGA-based LSTM architecture. Combined in an end-to-end framework, the approximation method's parameters are optimised and the architecture is configured to address the problem of high-performance LSTM execution in time-constrained applications. Quantitative evaluation on a real-life image captioning application indicates that the proposed methods required up to 6.5x less time to achieve the same application-level accuracy compared to a baseline method, while achieving an average of 25x higher accuracy under the same computation time constraints.

Connected through hyperedge HE-1 (induced by source paper "long short term memory networks in memristor crossbar arrays"):
  with members:
    - "mixed precision in memory computing"
    - "chipmunk a systolically scalable 0 9 mm 2 3 08 gop s mw 1 2 mw accelerator for near sensor recurrent neural network inference"
    - "the microsoft 2017 conversational speech recognition system"
    - "learning to detect violent videos using convolutional long short term memory"
    - "data recombination for neural semantic parsing"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-2 (induced by source paper "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"):
  with members:
    - "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"
    - "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-3 (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"
    - "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"
    - "fpgaconvnet a toolflow for mapping diverse convolutional neural networks on embedded fpgas"
    - "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)