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: "measuring numa effects with the stream benchmark"
  Abstract: Modern high-end machines feature multiple processor packages, each of which contains multiple independent cores and integrated memory controllers connected directly to dedicated physical RAM. These packages are connected via a shared bus, creating a system with a heterogeneous memory hierarchy. Since this shared bus has less bandwidth than the sum of the links to memory, aggregate memory bandwidth is higher when parallel threads all access memory local to their processor package than when they access memory attached to a remote package. #R##N#But, the impact of this heterogeneous memory architecture is not easily understood from vendor benchmarks. Even where these measurements are available, they provide only best-case memory throughput. This work presents a series of modifications to the well-known STREAM benchmark to measure the effects of NUMA on both a 48-core AMD Opteron machine and a 32-core Intel Xeon machine.

Connected through hyperedge HE-1 (induced by source paper "dimmwitted a study of main memory statistical analytics"):
  with members:
    - "an approximate efficient solver for lp rounding"
    - "near optimal coresets for least squares regression"
    - "mli an api for distributed machine learning"
    - "massively parallel sort merge joins in main memory multi core database systems"
    - "the madlib analytics library or mad skills the sql"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "building high level features using large scale unsupervised learning"
    - "a reliable effective terascale linear learning system"

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.PF(Performance)