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 "infaas a model less inference serving system"):
  Abstract: Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain key challenges. Developers must manually match the performance, accuracy, and cost constraints of their applications to decisions about selecting the right model and model optimizations, suitable hardware architectures, and auto-scaling configurations. These interacting decisions are difficult to make for users, especially when the application load varies, applications evolve, and the available resources vary over time. Thus, users often end up making decisions that overprovision resources. This paper introduces INFaaS, a model-less inference-as-a-service system that relieves users of making these decisions. INFaaS provides a simple interface allowing users to specify their inference task, and performance and accuracy requirements. To implement this interface, INFaaS generates and leverages model-variants, versions of a model that differ in resource footprints, latencies, costs, and accuracies. Based on the characteristics of the model-variants, INFaaS automatically navigates the decision space on behalf of users to meet user-specified objectives: (a) it selects a model, hardware architecture, and any compiler optimizations, and (b) it makes scaling and resource allocation decisions. By sharing models across users and hardware resources across models, INFaaS achieves up to 150x cost savings, 1.5x higher throughput, and violates latency objectives 1.5x less frequently, compared to Clipper and TensorFlow Serving.

Includes members:
  Member "one size does not fit all quantifying and exposing the accuracy latency trade off in machine learning cloud service apis via tolerance tiers":
    also connected through other hyperedges:
      (none)
  Member "dynamic space time scheduling for gpu inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "perseus characterizing performance and cost of multi tenant serving for cnn models")
  Member "pretzel opening the black box of machine learning prediction serving systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cloudburst stateful functions as a service")
  Member "trims transparent and isolated model sharing for low latency deep learninginference in function as a service environments":
    also connected through other hyperedges:
      (none)
  Member "tvm an automated end to end optimizing compiler for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "lit block wise intermediate representation training for model compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks")
  Member "scanner efficient video analysis at scale":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding interesting frames in deep video analytics a top k approach")

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)