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: "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
  Abstract: Developing efficient software and hardware has never been harder whether it is for a tiny IoT device or an Exascale supercomputer. Apart from the ever growing design and optimization complexity, there exist even more fundamental problems such as lack of interdisciplinary knowledge required for effective software/hardware co-design, and a growing technology transfer gap between academia and industry. #R##N#We introduce our new educational initiative to tackle these problems by developing Collective Knowledge (CK), a unified experimental framework for computer systems research and development. We use CK to teach the community how to make their research artifacts and experimental workflows portable, reproducible, customizable and reusable while enabling sustainable RD crowdsource experimentation across diverse platforms; share experimental results, models, visualizations; gradually expose more design and optimization choices using a simple JSON API; and ultimately build upon each other's findings. #R##N#As the first practical step, we have implemented customizable compiler autotuning, crowdsourced optimization of diverse workloads across Raspberry Pi 3 devices, reduced the execution time and code size by up to 40%, and applied machine learning to predict optimizations. We hope such approach will help teach students how to build upon each others' work to enable efficient and self-optimizing software/hardware/model stack for emerging workloads.

Connected through hyperedge HE-1 (induced by source paper "a survey on compiler autotuning using machine learning"):
  with members:
    - "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"
    - "compiler phase ordering as an orthogonal approach for reducing energy consumption"
    - "automatically tuning the gcc compiler to optimize the performance of applications running on embedded systems"
    - "collective mind part ii towards performance and cost aware software engineering as a natural science"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "autotuning opencl workgroup size for stencil patterns"
    - "iterative compilation on mobile devices"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "mlmodelscope a distributed platform for ml model evaluation and benchmarking at scale"):
  with members:
    - "aibench an industry standard internet service ai benchmark suite"
    - "model cards for model reporting"
    - "show your work improved reporting of experimental results"
    - "dlhub model and data serving for science"
    - "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-3 (induced by source paper "lost in translation exposing hidden compiler optimization opportunities"):
  with members:
    - "a survey on compiler autotuning using machine learning"
    - "the iot energy challenge a software perspective"
    - "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"
    - "entra whole systems energy transparency"

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.HC(Human-Computer Interaction)