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: "the need for speed of ai applications performance comparison of native vs browser based algorithm implementations"
  Abstract: AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence of client-side (federated) machine learning algorithms, driven by the interests of large corporations and startups alike. Apart from mathematical and algorithmic concerns, this trend especially demands new levels of computational efficiency from client environments. Consequently, this paper deals with the question of state-of-the-art performance by presenting a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms. Our results show that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. We analyze the results obtained and speculate on the reasons behind some surprises, rounding the paper off by outlining future possibilities as well as some of our own research efforts.

Connected through hyperedge HE-1 (induced by source paper "moving deep learning into web browser how far can we go"):
  with members:
    - "inverse reinforcement learning for video games"
    - "ai benchmark running deep neural networks on android smartphones"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "tensorflow a system for large scale machine learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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.AI(Artificial Intelligence)