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 "a computational model for tensor core units"):
  Abstract: To respond to the need of efficient training and inference of deep neural networks, a pletora of domain-specific hardware architectures have been introduced, such as Google Tensor Processing Units and NVIDIA Tensor Cores. A common feature of these architectures is a hardware circuit for efficiently computing a dense matrix multiplication of a given small size. In order to broad the class of algorithms that exploit these systems, we propose a computational model, named TCU model, that captures the ability to natively multiply small matrices. We then use the TCU model for designing fast algorithms for linear algebra problems, including dense and sparse matrix multiplication, FFT, integer multiplication, and polynomial evaluation. We finally highlight a relation between the TCU model and the external memory model.

Includes members:
  Member "accelerating reduction and scan using tensor core units":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gpu tensor cores for fast arithmetic reductions")
  Member "nvidia tensor core programmability performance precision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "modeling deep learning accelerator enabled gpus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "balancing efficiency and flexibility for dnn acceleration via temporal gpu systolic array integration")
  Member "mobile machine learning hardware at arm a systems on chip soc perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks")
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "benchmarking state of the art deep learning software tools":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on distributed machine learning")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "powers of tensors and fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")

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.DS(Data Structures and Algorithms)