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 "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"):
  Abstract: Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper addresses the memory capacity and bandwidth challenges of embedding layers and the associated tensor operations. We present our vertically integrated hardware/software co-design, which includes a custom DIMM module enhanced with near-data processing cores tailored for DL tensor operations. These custom DIMMs are populated inside a GPU-centric system interconnect as a remote memory pool, allowing GPUs to utilize for scalable memory bandwidth and capacity expansion. A prototype implementation of our proposal on real DL systems shows an average 6.2-17.6x performance improvement on state-of-the-art recommender systems.

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 "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-2 (induced by source paper "progressive compressed records taking a byte out of deep learning data")
  Member "buddy compression enabling larger memory for deep learning and hpc workloads on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low memory neural network training a technical report")
  Member "beyond human level accuracy computational challenges in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "beyond the memory wall a case for memory centric hpc system for deep learning")
  Member "competitive analysis system for theatrical movie releases based on movie trailer deep video representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recnmp accelerating personalized recommendation with near memory processing")
  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.LG(Machine Learning)