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: "comparing the performance of different x86 simd instruction sets for a medical imaging application on modern multi and manycore chips"
  Abstract: Single Instruction, Multiple Data (SIMD) vectorization is a major driver of performance in current architectures, and is mandatory for achieving good performance with codes that are limited by instruction throughput. We investigate the efficiency of different SIMD-vectorized implementations of the RabbitCT benchmark. RabbitCT performs 3D image reconstruction by back projection, a vital operation in computed tomography applications. The underlying algorithm is a challenge for vectorization because it consists, apart from a streaming part, also of a bilinear interpolation requiring scattered access to image data. We analyze the performance of SSE (128 bit), AVX (256 bit), AVX2 (256 bit), and IMCI (512 bit) implementations on recent Intel x86 systems. A special emphasis is put on the vector gather implementation on Intel Haswell and Knights Corner microarchitectures. Finally we discuss why GPU implementations perform much better for this specific algorithm.

Connected through hyperedge HE-1 (induced by source paper "quicker adc unlocking the hidden potential of product quantization with simd"):
  with members:
    - "derived codebooks for high accuracy nearest neighbor search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "faster population counts using avx2 instructions"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "subic a supervised structured binary code for image search"
    - "billion scale similarity search with gpus"
    - "cuckoo hash tables high performance hash tables for networking applications"

Connected through hyperedge HE-2 (induced by source paper "ifdk a scalable framework for instant high resolution image reconstruction"):
  with members:
    - "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"
    - "performance engineering for a medical imaging application on the intel xeon phi accelerator"
    - "pushing the limits for medical image reconstruction on recent standard multicore processors"

Connected through hyperedge HE-3 (induced by source paper "an analysis of core and chip level architectural features in four generations of intel server processors"):
  with members:
    - "an ecm based energy efficiency optimization approach for bandwidth limited streaming kernels on recent intel xeon processors"
    - "analysis of intel s haswell microarchitecture using the ecm model and microbenchmarks"
    - "quantifying performance bottlenecks of stencil computations using the execution cache memory model"
    - "pushing the limits for medical image reconstruction on recent standard multicore processors"
    - "chip level and multi node analysis of energy optimized lattice boltzmann cfd simulations"
    - "exploring performance and power properties of modern multicore chips via simple machine models"

Connected through hyperedge HE-4 (induced by source paper "accelerated nearest neighbor search with quick adc"):
  with members:
    - "searching in one billion vectors re rank with source 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.DC(Distributed, Parallel, and Cluster Computing)