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: "cloud based augmentation for mobile devices motivation taxonomies and open challenges"
  Abstract: Recently, Cloud-based Mobile Augmentation (CMA) approaches have gained remarkable ground from academia and industry. CMA is the state-of-the-art mobile augmentation model that employs resource-rich clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of resource-intensive mobile applications. Augmented mobile devices envision to perform extensive computations and to store big data beyond their intrinsic capabilities with least footprint and vulnerability. Researchers utilize varied cloud-based computing resources (e.g., distant clouds and nearby mobile nodes) to meet various computing requirements of mobile users. However, employing cloud-based computing resources is not a straightforward panacea. Comprehending critical factors (e.g., current state of mobile client and remote resources) that impact on augmentation process and optimum selection of cloud-based resource types are some challenges that hinder CMA adaptability. This paper comprehensively surveys the mobile augmentation domain and presents taxonomy of CMA approaches. The objectives of this study is to highlight the effects of remote resources on the quality and reliability of augmentation processes and discuss the challenges and opportunities of employing varied cloud-based resources in augmenting mobile devices. We present augmentation definition, motivation, and taxonomy of augmentation types, including traditional and cloud-based. We critically analyze the state-of-the-art CMA approaches and classify them into four groups of distant fixed, proximate fixed, proximate mobile, and hybrid to present a taxonomy. Vital decision making and performance limitation factors that influence on the adoption of CMA approaches are introduced and an exemplary decision making flowchart for future CMA approaches are presented. Impacts of CMA approaches on mobile computing is discussed and open challenges are presented as the future research directions.

Connected through hyperedge HE-1 (induced by source paper "optimal task assignment and power allocation for noma mobile edge computing networks"):
  with members:
    - "joint power and time allocation for noma mec offloading"
    - "energy efficient resource allocation in noma heterogeneous networks"
    - "delay minimization for noma mec offloading"
    - "edge computing aware noma for 5g networks"
    - "impact of non orthogonal multiple access on the offloading of mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "fog radio access networks mobility management interference mitigation and resource optimization"

Connected through hyperedge HE-2 (induced by source paper "mobile edge computing fog et al a survey and analysis of security threats and challenges"):
  with members:
    - "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"
    - "aqua computing coupling computing and communications"
    - "software defined networking a comprehensive survey"

Connected through hyperedge HE-3 (induced by source paper "continuous evolutionary and large scale a new perspective for automated mobile app testing"):
  with members:
    - "on device bug reporting for android applications"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "enabling mutation testing for android apps"
    - "automatically discovering reporting and reproducing android application crashes"

Connected through hyperedge HE-4 (induced by source paper "efficient radio resource management for wireless cellular networks with mobile edge computing"):
  with members:
    - "decentralized computation offloading game for mobile cloud computing"

Connected through hyperedge HE-5 (induced by source paper "ultra reliable cloud mobile computing with service composition and superposition coding"):
  with members:
    - "realizing the tactile internet haptic communications over next generation 5g cellular networks"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "inter layer per mobile optimization of cloud mobile computing a message passing approach"
    - "parallel and distributed methods for nonconvex optimization part i theory"
    - "ultra reliable communication in 5g wireless systems"
    - "joint optimization of radio resources and code partitioning in mobile cloud computing"
    - "distortion minimization in gaussian layered broadcast coding with successive refinement"

Connected through hyperedge HE-6 (induced by source paper "query processing for the internet of things coupling of device energy consumption and cloud infrastructure billing"):
  with members:
    - "energy consumption of visual sensor networks impact of spatio temporal coverage"
    - "distributed time frequency division multiple access protocol for wireless sensor networks"
    - "throughput distortion computation of generic matrix multiplication toward a computation channel for digital signal processing systems"
    - "internet of things iot a vision architectural elements and future directions"
    - "optimum transmission policies for battery limited energy harvesting nodes"
    - "optimal packet scheduling in an energy harvesting communication system"

Connected through hyperedge HE-7 (induced by source paper "software defined cloud computing architectural elements and open challenges"):
  with members:
    - "stratos a network aware orchestration layer for virtual middleboxes in clouds"
    - "internet of things iot a vision architectural elements and future directions"

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)