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: "revisiting co processing for hash joins on the coupled cpu gpu architecture"
  Abstract: Query co-processing on graphics processors (GPUs) has become an effective means to improve the performance of main memory databases. However, the relatively low bandwidth and high latency of the PCI-e bus are usually bottleneck issues for co-processing. Recently, coupled CPU-GPU architectures have received a lot of attention, e.g. AMD APUs with the CPU and the GPU integrated into a single chip. That opens up new opportunities for optimizing query co-processing. In this paper, we experimentally revisit hash joins, one of the most important join algorithms for main memory databases, on a coupled CPU-GPU architecture. Particularly, we study the fine-grained co-processing mechanisms on hash joins with and without partitioning. The co-processing outlines an interesting design space. We extend existing cost models to automatically guide decisions on the design space. Our experimental results on a recent AMD APU show that (1) the coupled architecture enables fine-grained co-processing and cache reuses, which are inefficient on discrete CPU-GPU architectures; (2) the cost model can automatically guide the design and tuning knobs in the design space; (3) fine-grained co-processing achieves up to 53%, 35% and 28% performance improvement over CPU-only, GPU-only and conventional CPU-GPU co-processing, respectively. We believe that the insights and implications from this study are initial yet important for further research on query co-processing on coupled CPU-GPU architectures.

Connected through hyperedge HE-1 (induced by source paper "hardware conscious stream processing a survey"):
  with members:
    - "briskstream scaling data stream processing on shared memory multicore architectures"
    - "streambox hbm stream analytics on high bandwidth hybrid memory"
    - "parallel index based stream join on a multicore cpu"
    - "join query optimization techniques for complex event processing applications"
    - "panjoin a partition based adaptive stream join"
    - "a survey of state management in big data processing systems"
    - "serverless computing current trends and open problems"
    - "ingestion indexing and retrieval of high velocity multidimensional sensor data on a single node"

Connected through hyperedge HE-2 (induced by source paper "a study of the fundamental performance characteristics of gpus and cpus for database analytics extended version"):
  with members:
    - "a memory bandwidth efficient hybrid radix sort on gpus"
    - "dissecting gpu memory hierarchy through microbenchmarking"

Connected through hyperedge HE-3 (induced by source paper "gpu based efficient join algorithms on hadoop"):
  with members:
    - "sharesskew an algorithm to handle skew for joins in mapreduce"
    - "efficient multi way theta join processing using mapreduce"
    - "fast updates on read optimized databases using multi core cpus"

Connected through hyperedge HE-4 (induced by source paper "generating custom code for efficient query execution on heterogeneous processors"):
  with members:
    - "boosting java performance using gpgpus"

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)