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 "neummu architectural support for efficient address translations in neural processing units"):
  Abstract: To satisfy the compute and memory demands of deep neural networks, neural processing units (NPUs) are widely being utilized for accelerating deep learning algorithms. Similar to how GPUs have evolved from a slave device into a mainstream processor architecture, it is likely that NPUs will become first class citizens in this fast-evolving heterogeneous architecture space. This paper makes a case for enabling address translation in NPUs to decouple the virtual and physical memory address space. Through a careful data-driven application characterization study, we root-cause several limitations of prior GPU-centric address translation schemes and propose a memory management unit (MMU) that is tailored for NPUs. Compared to an oracular MMU design point, our proposal incurs only an average 0.06% performance overhead.

Includes members:
  Member "deep learning recommendation model for personalization and recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "prema a predictive multi task scheduling algorithm for preemptible neural processing units":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference")
  Member "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning":
    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 "beyond the memory wall a case for memory centric hpc system for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "progressive compressed records taking a byte out of deep learning data")
  Member "beyond human level accuracy computational challenges in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling laws for neural language models")
  Member "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond the memory wall a case for memory centric hpc system for deep learning")
  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")
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")

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.AR(Hardware Architecture)