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 "herald optimizing heterogeneous dnn accelerators for edge devices"):
  Abstract: Recent advances in deep neural networks (DNNs) have made DNNs the backbone of many applications on edge devices such as face recognition, object detection, and so on. To deal with massive computation requirements of DNN inferences within stringent energy and latency constraints, DNN accelerator (i.e., hardware specialized forDNN inferences), have emerged as a promising solution. Such advancement of hardware supporting DNNs has led to multiple DNN-based applications running at the same time on edge devices. They often run in parallel as background processes or as sub-tasks of a complex application. Thus, DNN workloads on a DNN accelerator now include a variety of layer operations and sizes from DNN models for diverse applications making them heterogeneous in layer granularity. Such heterogeneous workloads introduce a new major challenge for monolithic DNN accelerators because the efficiency of DNN accelerators relies on its dataflow, and different DNN layer types and shapes prefer different dataflows. In this work, we propose to tackle this challenge by designing heterogeneous DNN accelerators (HDAs) that deploy multiple DNN accelerators each optimized for different layer shapes and operations. To enable this approach, we propose HERALD, an optimization framework that explores the design space an HDA and layer schedules. Design time-optimized HDAs with the best energy-delay-product (EDP) HERALD identified provided 24.93% EDP benefits with 16.1% latency and 7.6% energy benefits on average across workloads and accelerators we evaluate compared to the best case of monolithic accelerators for each evaluation setting by deploying two complementary-style DNN accelerators.HERALD's scheduler employs heuristics that exploit the characteristics of DNN workloads, which provided 6.4% better EDP on average compared to a baseline scheduler.

Includes members:
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "ai benchmark running deep neural networks on android smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "understanding reuse performance and hardware cost of dnn dataflows a data centric approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autodnnchip an automated dnn chip predictor and builder for both fpgas and asics")
  Member "learning depth from single images with deep neural network embedding focal length":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "deeprebirth accelerating deep neural network execution on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors")
  Member "end to end global to local cnn learning for hand pose recovery in depth data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic future prediction for video scene understanding")

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.DC(Distributed, Parallel, and Cluster Computing)