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: "codex bit flexible encoding for streaming based fpga acceleration of dnns"
  Abstract: This paper proposes CodeX, an end-to-end framework that facilitates encoding, bitwidth customization, fine-tuning, and implementation of neural networks on FPGA platforms. CodeX incorporates nonlinear encoding to the computation flow of neural networks to save memory. The encoded features demand significantly lower storage compared to the raw full-precision activation values; therefore, the execution flow of CodeX hardware engine is completely performed within the FPGA using on-chip streaming buffers with no access to the off-chip DRAM. We further propose a fully-automated algorithm inspired by reinforcement learning which determines the customized encoding bitwidth across network layers. CodeX full-stack framework comprises of a compiler which takes a high-level Python description of an arbitrary neural network architecture. The compiler then instantiates the corresponding elements from CodeX Hardware library for FPGA implementation. Proof-of-concept evaluations on MNIST, SVHN, and CIFAR-10 datasets demonstrate an average of 4.65x throughput improvement compared to stand-alone weight encoding. We further compare CodeX with six existing full-precision DNN accelerators on ImageNet, showing an average of 3.6x and 2.54x improvement in throughput and performance-per-watt, respectively.

Connected through hyperedge HE-1 (induced by source paper "ascai adaptive sampling for acquiring compact ai"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "a novel channel pruning method for deep neural network compression"
    - "data driven sparse structure selection for deep neural networks"
    - "large scale evolution of image classifiers"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement 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.LG(Machine Learning)