human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "mlmodelscope a distributed platform for ml model evaluation and benchmarking at scale"):
  with members:
    - "model cards for model reporting"
    - "show your work improved reporting of experimental results"
    - "dlhub model and data serving for science"
    - "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "edge aibench towards comprehensive end to end edge computing benchmarking"):
  with members:
    - "federated machine learning concept and applications"
    - "ai benchmark running deep neural networks on android smartphones"
    - "edgebench benchmarking edge computing platforms"
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "facenet a unified embedding for face recognition and clustering"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-3 (induced by source paper "the design and implementation of a scalable dl benchmarking platform"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "dynamic control flow in large scale machine learning"
    - "tbd benchmarking and analyzing deep neural network training"
    - "tensorflow serving flexible high performance ml serving"
    - "clipper a low latency online prediction serving system"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "benchcouncil s view on benchmarking ai and other emerging workloads"):
  with members:
    - "mlperf inference benchmark"
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "mlperf training benchmark"
    - "data motif based proxy benchmarks for big data and ai workloads"
    - "benchmark analysis of representative deep neural network architectures"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"

Question: Please tell me which class the center node 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)