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 "on linear learning with manycore processors"):
  Abstract: A new generation of manycore processors is on the rise that offers dozens and more cores on a chip and, in a sense, fuses host processor and accelerator. In this paper we target the efficient training of generalized linear models on these machines. We propose a novel approach for achieving parallelism which we call Heterogeneous Tasks on Homogeneous Cores (HTHC). It divides the problem into multiple fundamentally different tasks, which themselves are parallelized. For evaluation, we design a detailed, architecture-cognizant implementation of our scheme on a recent 72-core Knights Landing processor that is adaptive to the cache, memory, and core structure. Our library efficiently supports dense and sparse datasets as well as 4-bit quantized data for further possible gains in performance. We show benchmarks for Lasso and SVM with different data sets against straightforward parallel implementations and prior software. In particular, for Lasso on dense data, we improve the state-of-the-art by an order of magnitude.

Includes members:
  Member "parallel approximation of the maximum likelihood estimation for the prediction of large scale geostatistics simulations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geostatistical modeling and prediction using mixed precision tile cholesky factorization")
  Member "partitioning compute units in cnn acceleration for statistical memory traffic shaping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "restructuring batch normalization to accelerate cnn training")
  Member "faster coordinate descent via adaptive importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "scaling deep learning on gpu and knights landing clusters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "efficient use of limited memory accelerators for linear learning on heterogeneous systems":
    also connected through other hyperedges:
      (none)
  Member "safe adaptive importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets")
  Member "primal dual rates and certificates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cola decentralized linear learning")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.PF(Performance)