human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "branch prediction is not a solved problem measurements opportunities and future directions"):
  Abstract: Modern branch predictors predict the vast majority of conditional branch instructions with near-perfect accuracy, allowing superscalar, out-of-order processors to maximize speculative efficiency and thus performance. However, this impressive overall effectiveness belies a substantial missed opportunity in single-threaded instructions per cycle (IPC). For example, we show that correcting the mispredictions made by the state-of-the-art TAGE-SC-L branch predictor on SPECint 2017 would improve IPC by margins similar to an advance in process technology node. #R##N#In this work, we measure and characterize these mispredictions. We find that they categorically arise from either (1) a small number of systematically hard-to-predict (H2P) branches; or (2) rare branches with low dynamic execution counts. Using data from SPECint 2017 and additional large code footprint applications, we quantify the occurrence and IPC impact of these two categories. We then demonstrate that increasing the resources afforded to existing branch predictors does not alone address the root causes of most mispredictions. This leads us to reexamine basic assumptions in branch prediction and to propose new research directions that, for example, deploy machine learning to improve pattern matching for H2Ps, and use on-chip phase learning to track long-term statistics for rare branches.

Includes members:
  Member "improving branch prediction by modeling global history with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of machine learning applied to computer architecture design")
  Member "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")

Question: Please tell me which class the source paper 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)