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: "automatic compiler based fpga accelerator for cnn training"
  Abstract: Training of convolutional neural networks (CNNs)on embedded platforms to support on-device learning is earning vital importance in recent days. Designing flexible training hard-ware is much more challenging than inference hardware, due to design complexity and large computation/memory requirement. In this work, we present an automatic compiler-based FPGA accelerator with 16-bit fixed-point precision for complete CNNtraining, including Forward Pass (FP), Backward Pass (BP) and Weight Update (WU). We implemented an optimized RTL library to perform training-specific tasks and developed an RTL compiler to automatically generate FPGA-synthesizable RTL based on user-defined constraints. We present a new cyclic weight storage/access scheme for on-chip BRAM and off-chip DRAMto efficiently implement non-transpose and transpose operations during FP and BP phases, respectively. Representative CNNs for CIFAR-10 dataset are implemented and trained on Intel Stratix 10-GX FPGA using proposed hardware architecture, demonstrating up to 479 GOPS performance.

Connected through hyperedge HE-1 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms"):
  with members:
    - "graphsaint graph sampling based inductive learning method"
    - "link prediction based on graph neural networks"
    - "accurate efficient and scalable graph embedding"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "single net continual learning with progressive segmented training pst"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "efficient network construction through structural plasticity"
    - "regularize expand and compress multi task based lifelong learning via nonexpansive automl"
    - "large scale incremental learning"
    - "towards efficient neural networks on a chip joint hardware algorithm approaches"
    - "revisiting distillation and incremental classifier learning"
    - "reinforced continual learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

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)