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: "cavbench a benchmark suite for connected and autonomous vehicles"
  Abstract: Connected and autonomous vehicles (CAVs) have recently attracted a significant amount of attention both from researchers and industry. Numerous studies targeting algorithms, software frameworks, and applications on the CAVs scenario have emerged. Meanwhile, several pioneer efforts have focused on the edge computing system and architecture design for the CAVs scenario and provided various heterogeneous platform prototypes for CAVs. However, a standard and comprehensive application benchmark for CAVs is missing, hindering the study of these emerging computing systems. To address this challenging problem, we present CAVBench, the first benchmark suite for the edge computing system in the CAVs scenario. CAVBench is comprised of six typical applications covering four dominate CAVs scenarios and takes four datasets as standard input. CAVBench provides quantitative evaluation results via application and system perspective output metrics. We perform a series of experiments and acquire three systemic characteristics of the applications in CAVBench. First, the operation intensity of the applications is polarized, which explains why heterogeneous hardware is important for a CAVs computing system. Second, all applications in CAVBench consume high memory bandwidth, so the system should be equipped with high bandwidth memory or leverage good memory bandwidth management to avoid the performance degradation caused by memory bandwidth competition. Third, some applications have worse data/instruction locality based on the cache miss observation, so the computing system targeting these applications should optimize the cache architecture. Last, we use the CAVBench to evaluate a typical edge computing platform and present the quantitative and qualitative analysis of the benchmarking results.

Connected through hyperedge HE-1 (induced by source paper "openei an open framework for edge intelligence"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "yolov3 an incremental improvement"
    - "in datacenter performance analysis of a tensor processing unit"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "mobile edge intelligence and computing for the internet of vehicles"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "client edge cloud hierarchical federated learning"
    - "a mobility aware vehicular caching scheme in content centric networks model and optimization"
    - "edge assisted hierarchical federated learning with non iid data"
    - "federated learning strategies for improving communication efficiency"
    - "exploiting mobility in cache assisted d2d networks performance analysis and optimization"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "follow me at the edge mobility aware dynamic service placement for mobile edge computing"

Connected through hyperedge HE-3 (induced by source paper "f cooper feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "cooper cooperative perception for connected autonomous vehicles based on 3d point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "cascade r cnn delving into high quality object detection"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "defog fog computing benchmarks"):
  with members:
    - "cloud benchmarking for maximising performance of scientific applications"
    - "yolov3 an incremental improvement"
    - "edgebench benchmarking edge computing platforms"
    - "enorm a framework for edge node resource management"
    - "doclite a docker based lightweight cloud benchmarking tool"
    - "challenges and opportunities in edge computing"
    - "container based cloud virtual machine benchmarking"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-5 (induced by source paper "pi edge a low power edge computing system for real time autonomous driving services"):
  with members:
    - "pirvs an advanced visual inertial slam system with flexible sensor fusion and hardware co design"

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)