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: "improving branch prediction by modeling global history with convolutional neural networks"
  Abstract: CPU branch prediction has hit a wall--existing techniques achieve near-perfect accuracy on 99% of static branches, and yet the mispredictions that remain hide major performance gains. In a companion report, we show that a primary source of mispredictions is a handful of systematically hard-to-predict branches (H2Ps), e.g. just 10 static instructions per SimPoint phase in SPECint 2017. The lost opportunity posed by these mispredictions is significant to the CPU: 14.0% in instructions-per-cycle (IPC) on Intel SkyLake and 37.4% IPC when the pipeline is scaled four-fold, on par with gains from process technology. However, up to 80% of this upside is unreachable by the best known branch predictors, even when afforded exponentially more resources. #R##N#New approaches are needed, and machine learning (ML) provides a palette of powerful predictors. A growing body of work has shown that ML models are deployable within the microarchitecture to optimize hardware at runtime, and are one way to customize CPUs post-silicon by training to customer applications. We develop this scenario for branch prediction using convolutional neural networks (CNNs) to boost accuracy for H2Ps. Step-by-step, we (1) map CNNs to the global history data used by existing branch predictors; (2) show how CNNs improve H2P prediction in SPEC 2017; (3) adapt 2-bit CNN inference to the constraints of current branch prediction units; and (4) establish that CNN helper predictors are reusable across application executions on different inputs, enabling us to amortize offline training and deploy ML pattern matching to improve IPC.

Connected through hyperedge HE-1 (induced by source paper "a survey of machine learning applied to computer architecture design"):
  with members:
    - "a static analysis based cross architecture performance prediction using machine learning"
    - "optimizing routerless network on chip designs an innovative learning based framework"
    - "learning multi agent communication under limited bandwidth restriction for internet packet routing"
    - "learning based application agnostic 3d noc design for heterogeneous manycore systems"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the case for learned index structures"
    - "feature selection a data perspective"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-2 (induced by source paper "branch prediction is not a solved problem measurements opportunities and future directions"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

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.DC(Distributed, Parallel, and Cluster Computing)