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 "aibench an industry standard internet service ai benchmark suite"):
  Abstract: Today's Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many innovative AI algorithms, systems, and architectures are proposed, and thus the importance of benchmarking and evaluating them rises. However, modern Internet services adopt a microservice-based architecture and consist of various modules. The diversity of these modules and complexity of execution paths, the massive scale and complex hierarchy of datacenter infrastructure, the confidential issues of data sets and workloads pose great challenges to benchmarking. In this paper, we present the first industry-standard Internet service AI benchmark suite---AIBench with seventeen industry partners, including several top Internet service providers. AIBench provides a highly extensible, configurable, and flexible benchmark framework that contains loosely coupled modules. We identify sixteen prominent AI problem domains like learning to rank, each of which forms an AI component benchmark, from three most important Internet service domains: search engine, social network, and e-commerce, which is by far the most comprehensive AI benchmarking effort. On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales. The specifications, source code, and performance numbers are publicly available from the benchmark council web site this http URL.

Includes members:
  Member "edge aibench towards comprehensive end to end edge computing benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ai oriented medical workload allocation for hierarchical cloud edge device computing")
  Member "ranking distillation learning compact ranking models with high performance for recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "frustrated with replicating claims of a shared model a solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "bigdatabench a scalable and unified big data and ai benchmark suite":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "data motifs a lens towards fully understanding big data and ai workloads":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "tbd benchmarking and analyzing deep neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "in datacenter performance analysis of a tensor processing unit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "vggface2 a dataset for recognising faces across pose and age":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

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.CV(Computer Vision and Pattern Recognition)