human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures"):
  Abstract: The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators. However, designing DNN accelerators is non-trivial as it often takes months/years and requires cross-disciplinary knowledge. To enable fast and effective DNN accelerator development, we propose DNN-Chip Predictor, an analytical performance predictor which can accurately predict DNN accelerators' energy, throughput, and latency prior to their actual implementation. Our Predictor features two highlights: (1) its analytical performance formulation of DNN ASIC/FPGA accelerators facilitates fast design space exploration and optimization; and (2) it supports DNN accelerators with different algorithm-to-hardware mapping methods (i.e., dataflows) and hardware architectures. Experiment results based on 2 DNN models and 3 different ASIC/FPGA implementations show that our DNN-Chip Predictor's predicted performance differs from those of chip measurements of FPGA/ASIC implementation by no more than 17.66% when using different DNN models, hardware architectures, and dataflows. We will release code upon acceptance.

Includes members:
  Member "skynet a champion model for dac sdc on low power object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems")
  Member "e2 train training state of the art cnns with over 80 energy savings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards byzantine resilient learning in decentralized systems")
  Member "dual dynamic inference enabling more efficient adaptive and controllable deep inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "dnn dataflow choice is overrated":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross channel intragroup sparsity neural network")
  Member "scale sim systolic cnn accelerator simulator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ecnn a block based and highly parallel cnn accelerator for edge inference")
  Member "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pre defined sparsity for low complexity convolutional neural networks")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")

Question: Please tell me which class the source paper 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)