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: "collective mind part ii towards performance and cost aware software engineering as a natural science"
  Abstract: Nowadays, engineers have to develop software often without even knowing which hardware it will eventually run on in numerous mobile phones, tablets, desktops, laptops, data centers, supercomputers and cloud services. Unfortunately, optimizing compilers are not keeping pace with ever increasing complexity of computer systems anymore and may produce severely underperforming executable codes while wasting expensive resources and energy. #R##N#We present our practical and collaborative solution to this problem via light-weight wrappers around any software piece when more than one implementation or optimization choice available. These wrappers are connected with a public Collective Mind autotuning infrastructure and repository of knowledge (c-mind.org/repo) to continuously monitor various important characteristics of these pieces (computational species) across numerous existing hardware configurations together with randomly selected optimizations. Similar to natural sciences, we can now continuously track winning solutions (optimizations for a given hardware) that minimize all costs of a computation (execution time, energy spent, code size, failures, memory and storage footprint, optimization time, faults, contentions, inaccuracy and so on) of a given species on a Pareto frontier along with any unexpected behavior. The community can then collaboratively classify solutions, prune redundant ones, and correlate them with various features of software, its inputs (data sets) and used hardware either manually or using powerful predictive analytics techniques. Our approach can then help create a large, realistic, diverse, representative, and continuously evolving benchmark with related optimization knowledge while gradually covering all possible software and hardware to be able to predict best optimizations and improve compilers and hardware depending on usage scenarios and requirements.

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"
    - "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
    - "automatically tuning the gcc compiler to optimize the performance of applications running on embedded systems"
    - "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 "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "integrating a large scale testing campaign in the ck framework"
    - "introducing slambench a performance and accuracy benchmarking methodology for slam"
    - "community driven reviewing and validation of publications"
    - "finding representative sets of optimizations for adaptive multiversioning applications"
    - "imagenet large scale visual recognition challenge"

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.SE(Software Engineering)