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: "bigdatabench a big data benchmark suite from internet services"
  Abstract: As architecture, systems, and data management communities pay greater attention to innovative big data systems and architecture, the pressure of benchmarking and evaluating these systems rises. However, the complexity, diversity, frequently changed workloads, and rapid evolution of big data systems raise great challenges in big data benchmarking. Considering the broad use of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads, which is the prerequisite for evaluating big data systems and architecture. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above.

Connected through hyperedge HE-1 (induced by source paper "tlb and pagewalk performance in multicore architectures with large die stacked dram cache"):
  with members:
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-2 (induced by source paper "aibench an industry standard internet service ai benchmark suite"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "tensor train decomposition on tensorflow t3f"
    - "a practical randomized cp tensor decomposition"
    - "software for sparse tensor decomposition on emerging computing architectures"
    - "spartan scalable parafac2 for large sparse data"
    - "tensor regression meets gaussian processes"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "a unified optimization approach for sparse tensor operations on gpus"

Connected through hyperedge HE-4 (induced by source paper "processing data where it makes sense enabling in memory computation"):
  with members:
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "architectural techniques for improving nand flash memory reliability"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "techniques for shared resource management in systems with throughput processors"
    - "errors in flash memory based solid state drives analysis mitigation and recovery"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "using ecc dram to adaptively increase memory capacity"

Connected through hyperedge HE-5 (induced by source paper "a workload and programming ease driven perspective of processing in memory"):
  with members:
    - "processing data where it makes sense enabling in memory computation"
    - "in dram bulk bitwise execution engine"
    - "rowhammer a retrospective"
    - "accelerating bulk bit wise x n or operation in processing in dram platform"
    - "shouji a fast and efficient pre alignment filter for sequence alignment"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "memristive devices for computation in memory"
    - "what your dram power models are not telling you lessons from a detailed experimental study"

Connected through hyperedge HE-6 (induced by source paper "hpc ai500 a benchmark suite for hpc ai systems"):
  with members:
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "tbd benchmarking and analyzing deep neural network training"
    - "globenet convolutional neural networks for typhoon eye tracking from remote sensing imagery"
    - "scaling grpc tensorflow on 512 nodes of cori supercomputer"
    - "benchmarking state of the art deep learning software tools"

Connected through hyperedge HE-7 (induced by source paper "landscape of big medical data a pragmatic survey on prioritized tasks"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "scalable and accurate deep learning for electronic health records"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "a survey on deep learning in medical image analysis"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "a unified framework for tumor proliferation score prediction in breast histopathology"
    - "deep activity recognition models with triaxial accelerometers"

Connected through hyperedge HE-8 (induced by source paper "edge aibench towards comprehensive end to end edge computing benchmarking"):
  with members:
    - "aibench an industry standard internet service ai benchmark suite"
    - "federated machine learning concept and applications"
    - "ai benchmark running deep neural networks on android smartphones"
    - "edgebench benchmarking edge computing platforms"
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "facenet a unified embedding for face recognition and clustering"

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.DB(Databases)