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: "managing service heterogeneity using osmotic computing"
  Abstract: Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization

Connected through hyperedge HE-1 (induced by source paper "a survey on the security and the evolution of osmotic and catalytic computing for 5g networks"):
  with members:
    - "5g nr jamming spoofing and sniffing threat assessment and mitigation"
    - "a framework for mitigating zero day attacks in iot"
    - "nhad neuro fuzzy based horizontal anomaly detection in online social networks"
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"
    - "rehand secure region based fast handover with user anonymity for small cell networks in 5g"
    - "robust physical layer security for power domain non orthogonal multiple access based hetnets and hudns sic avoidance at eavesdroppers"
    - "a survey on the security of pervasive online social networks posns"
    - "a survey of physical layer security techniques for 5g wireless networks and challenges ahead"

Connected through hyperedge HE-2 (induced by source paper "security privacy and trust for smart mobile internet of things m iot a survey"):
  with members:
    - "a framework for mitigating zero day attacks in iot"
    - "nhad neuro fuzzy based horizontal anomaly detection in online social networks"
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "advertising in the iot era vision and challenges"
    - "extension of mih to support fpmipv6 for optimized heterogeneous handover"
    - "securing wireless communications of the internet of things from the physical layer an overview"
    - "traffic aware patching for cyber security in mobile iot"
    - "trifecta security energy efficiency and communication capacity comparison for wireless iot devices"

Connected through hyperedge HE-3 (induced by source paper "a survey on the security of pervasive online social networks posns"):
  with members:
    - "a framework for mitigating zero day attacks in iot"
    - "nhad neuro fuzzy based horizontal anomaly detection in online social networks"
    - "self enforcing game theory based resource allocation for lorawan assisted public safety communications"
    - "netspam a network based spam detection framework for reviews in online social media"
    - "a privacy preserving community based p2p osns using broadcast encryption supporting recommendation mechanism"
    - "extension of mih to support fpmipv6 for optimized heterogeneous handover"
    - "analyzing resilience of interest based social networks against node and link failures"
    - "anomaly detection in online social networks"

Connected through hyperedge HE-4 (induced by source paper "research challenges in nextgen service orchestration"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "a comparison of reinforcement learning techniques for fuzzy cloud auto scaling"
    - "mobile cloud business process management system for the internet of things a survey"
    - "enorm a framework for edge node resource management"
    - "occupy the cloud distributed computing for the 99"
    - "on using micro clouds to deliver the fog"
    - "improving spark application throughput via memory aware task co location a mixture of experts approach"
    - "daleel simplifying cloud instance selection using machine learning"

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)