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: "enorm a framework for edge node resource management"
  Abstract: Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokeMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20% - 80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95\%. These results highlight the potential of fog computing for improving the quality of service and experience.

Connected through hyperedge HE-1 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning"):
  with members:
    - "feasibility of fog computing"
    - "defog fog computing benchmarks"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "fog radio access networks mobility management interference mitigation and resource optimization"
    - "a survey and taxonomy of resource optimisation for executing bag of task applications on public clouds"
    - "challenges and opportunities in edge computing"
    - "decentralized computation offloading game for mobile cloud computing"

Connected through hyperedge HE-2 (induced by source paper "lucid a practical lightweight deep learning solution for ddos attack detection"):
  with members:
    - "a practical approach to detection of distributed denial of service attacks using a hybrid detection method"
    - "on evaluating adversarial robustness"
    - "understanding convolutional neural networks for text classification"
    - "automated vulnerability detection in source code using deep representation learning"
    - "deep convolutional neural networks for interpretable analysis of eeg sleep stage scoring"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "modelling fog offloading performance"):
  with members:
    - "performance estimation of container based cloud to fog offloading"
    - "defog fog computing benchmarks"
    - "resource management in fog edge computing a survey"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"
    - "challenges and opportunities in edge computing"

Connected through hyperedge HE-4 (induced by source paper "priority based fair scheduling in edge computing"):
  with members:
    - "distributed and application aware task scheduling in edge clouds"
    - "dyverse dynamic vertical scaling in multi tenant edge environments"
    - "challenges and opportunities in edge computing"
    - "fog computing focusing on mobile users at the edge"

Connected through hyperedge HE-5 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "are fpgas suitable for edge computing"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "fog computing a taxonomy survey and future directions"
    - "data analytics service composition and deployment on edge devices"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"

Connected through hyperedge HE-6 (induced by source paper "defog fog computing benchmarks"):
  with members:
    - "cloud benchmarking for maximising performance of scientific applications"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "yolov3 an incremental improvement"
    - "edgebench benchmarking edge computing platforms"
    - "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-7 (induced by source paper "exploring the effectiveness of service decomposition in fog computing architecture for the internet of things"):
  with members:
    - "fog computing for sustainable smart cities a survey"
    - "microservices granularity vs performance"
    - "privacy preserving personal model training"
    - "a hybrid approach for data analytics for internet of things"
    - "valorising the iot databox creating value for everyone"
    - "a survey on internet of things from industrial market perspective"

Connected through hyperedge HE-8 (induced by source paper "the fog development kit a development platform for sdn based edge fog systems"):
  with members:
    - "image classification on iot edge devices profiling and modeling"
    - "profiling and improving the duty cycling performance of linux based iot devices"
    - "implementation and analysis of quic for mqtt"

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)