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: "distributed scheduling of event analytics across edge and cloud"
  Abstract: Internet of Things (IoT) domains generate large volumes of high velocity event streams from sensors, which need to be analyzed with low latency to drive decisions. Complex Event Processing (CEP) is a Big Data technique to enable such analytics, and is traditionally performed on Cloud Virtual Machines (VM). Leveraging captive IoT edge resources in combination with Cloud VMs can offer better performance, flexibility and monetary costs for CEP. Here, we formulate an optimization problem for energy-aware placement of CEP queries, composed as an analytics dataflow, across a collection of edge and Cloud resources, with the goal of minimizing the end-to-end latency for the dataflow. We propose a Genetic Algorithm (GA) meta-heuristic to solve this problem, and compare it against a brute-force optimal algorithm (BF). We perform detailed real-world benchmarks on the compute, network and energy capacity of edge and Cloud resources. These results are used to define a realistic and comprehensive simulation study that validates the BF and GA solutions for 45 diverse CEP dataflows, LAN and WAN setup, and different edge resource availability. We compare the GA and BF solutions against random and Cloud-only baselines for different configurations, for a total of 1764 simulation runs. Our study shows that GA is within 97% of the optimal BF solution that takes hours, maps dataflows with 4 - 50 queries in 1 - 26 secs, and only fails to offer a feasible solution <= 20% of the time.

Connected through hyperedge HE-1 (induced by source paper "characterizing application scheduling on edge fog and cloud computing resources"):
  with members:
    - "violet a large scale virtual environment for internet of things"
    - "adaptive energy aware scheduling of dynamic event analytics across edge and cloud resources"
    - "slaq quality driven scheduling for distributed machine learning"
    - "arm wrestling with big data a study of commodity arm64 server for big data workloads"
    - "a manifesto for future generation cloud computing research directions for the next decade"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "big data and fog computing"

Connected through hyperedge HE-2 (induced by source paper "challenges in designing edge based middlewares for the internet of things a survey"):
  with members:
    - "cefiot a fault tolerant iot architecture for edge and cloud"
    - "fogstore toward a distributed data store for fog computing"
    - "foggis fog computing for geospatial big data analytics"
    - "task allocation for distributed stream processing"
    - "cloud4iot a heterogeneous distributed and autonomic cloud platform for the iot"

Connected through hyperedge HE-3 (induced by source paper "violet a large scale virtual environment for internet of things"):
  with members:
    - "mathbb echo an adaptive orchestration platform for hybrid dataflows across cloud and edge"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "riotbench a real time iot benchmark for distributed stream processing platforms"
    - "plug and play bench simplifying big data benchmarking using containers"
    - "fog computing principles architectures and applications"

Connected through hyperedge HE-4 (induced by source paper "towards a data driven iot software architecture for smart city utilities"):
  with members:
    - "mathbb echo an adaptive orchestration platform for hybrid dataflows across cloud and edge"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-5 (induced by source paper "adaptive energy aware scheduling of dynamic event analytics across edge and cloud resources"):
  with members:
    - "violet a large scale virtual environment for internet of things"
    - "demystifying fog computing characterizing architectures applications and abstractions"

Connected through hyperedge HE-6 (induced by source paper "mathbb echo an adaptive orchestration platform for hybrid dataflows across cloud and edge"):
  with members:
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "riotbench a real time iot benchmark for distributed stream processing platforms"
    - "yolo9000 better faster stronger"
    - "a gap analysis of internet of things platforms"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "demystifying fog computing characterizing architectures applications and abstractions"):
  with members:
    - "fog computing principles architectures and applications"
    - "fog computing focusing on mobile users at the edge"
    - "the internet of flying things opportunities and challenges with airborne fog computing and mobile cloud in the clouds"
    - "holistic measures for evaluating prediction models in smart grids"
    - "towards a practical architecture for india centric internet of things"

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)