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 "deeprecsys a system for optimizing end to end at scale neural recommendation inference"):
  Abstract: Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thus, improving the execution efficiency of neural recommendation directly translates into infrastructure capacity saving. In this paper, we devise a novel end-to-end modeling infrastructure, DeepRecInfra, that adopts an algorithm and system co-design methodology to custom-design systems for recommendation use cases. Leveraging the insights from the recommendation characterization, a new dynamic scheduler, DeepRecSched, is proposed to maximize latency-bounded throughput by taking into account characteristics of inference query size and arrival patterns, recommendation model architectures, and underlying hardware systems. By doing so, system throughput is doubled across the eight industry-representative recommendation models. Finally, design, deployment, and evaluation in at-scale production datacenter shows over 30% latency reduction across a wide variety of recommendation models running on hundreds of machines.

Includes members:
  Member "the architectural implications of facebook s dnn based personalized recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting parallelism opportunities with deep learning frameworks")
  Member "deep learning recommendation model for personalization and recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "mixed dimension embeddings with application to memory efficient recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations")
  Member "mlperf inference benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mnn a universal and efficient inference engine")
  Member "prema a predictive multi task scheduling algorithm for preemptible neural processing units":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neummu architectural support for efficient address translations in neural processing units")
  Member "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation")
  Member "compositional embeddings using complementary partitions for memory efficient recommendation systems":
    also connected through other hyperedges:
      (none)
  Member "mlperf training benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")

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)