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 "perseus characterizing performance and cost of multi tenant serving for cnn models"):
  Abstract: Deep learning models are increasingly used for end-user applications, supporting both novel features, such as facial recognition, and traditional features, such as web search. To accommodate high inference throughput, it is common to host a single pre-trained Convolutional Neural Network (CNN) in dedicated cloud-based servers with hardware accelerators such as Graphics Processing Units (GPUs). However, GPUs can be orders of magnitude more expensive than traditional Central Processing Unit (CPU) servers. Under-utilized server resources brought about by dynamic workloads can influence provisioning decisions, which may result in inflated serving costs. One potential way to alleviate this problem is by allowing hosted models to share the underlying resources, which we refer to as multi-tenant inference serving. One of the key challenges is maximizing the resource efficiency for multi-tenant serving given hardware with diverse characteristics, models with unique response time Service Level Agreement (SLA), and dynamic inference workloads. In this paper, we present Perseus, a measurement framework that provides the basis for understanding the performance and cost trade-offs of multi-tenant model serving. We implemented Perseus in Python atop a popular cloud inference server called Nvidia TensorRT Inference Server. Leveraging Perseus, we evaluated the inference throughput and cost for various serving deployments and demonstrated that multi-tenant model serving can lead to up to 12% cost reduction.

Includes members:
  Member "barista efficient and scalable serverless serving system for deep learning prediction services":
    also connected through other hyperedges:
      (none)
  Member "no dnn left behind improving inference in the cloud with multi tenancy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "characterizing the deep neural networks inference performance of mobile applications")
  Member "dynamic space time scheduling for gpu inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling of deep neural network dnn placement and inference in edge computing")
  Member "mlperf inference benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference")
  Member "infaas a model less inference serving system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling of deep neural network dnn placement and inference in edge computing")
  Member "salus fine grained gpu sharing primitives for deep learning applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "infaas a model less inference serving system")
  Member "auto tuning neural network quantization framework for collaborative inference between the cloud and edge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "artificial intelligence for digital agriculture at scale techniques policies and challenges")
  Member "deep learning inference in facebook data centers characterization performance optimizations and hardware implications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving efficiency in neural network accelerator using operands hamming distance optimization")

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)