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: "less is more participation incentives in d2d enhanced mobile edge computing under infectious ddos attacks"
  Abstract: Device-to-device (D2D) computation offloading has recently been proposed to enhance mobile edge computing (MEC) performance by exploiting spare computing resources in proximity user devices, thereby alleviating computation burdens from the network infrastructure and enabling truly pervasive edge computing. A key challenge in this new mobile computing paradigm is how to provide self-interested users with incentives to participate in D2D computing. Although incentive mechanism design has been intensively studied in the literature, this paper considers a much more challenging yet much under-investigated problem in which user incentives are complexly coupled with security risks, which is extremely important since D2D-enhanced MEC systems are vulnerable to distributed attacks, such as distributed denial of service (DDoS) attacks, due to its autonomous nature. In this paper, we build a novel mathematical framework incorporating game theory and classic epidemic models to investigate the interplay between user incentives and security risks in D2D-enhanced MEC systems under infectious DDoS attacks. A key result derived from this analysis is a phase change effect between persistent and non-persistent DDoS attacks, which is significantly different in nature from classic epidemic results for non-strategic users. Based on this, we determine the optimal reward in a contract-based incentive mechanism that the network operator should offer to users in order to maximize the operator's utility. The optimal solution exhibits an interesting "Less is More" effect: although giving users a higher reward promote more participation, it may harm the operator's utility. This is because too much participation fosters persistent DDoS attack and as a result, the \textit{effective} participation level does not improve. Extensive simulations are carried out to verify the analytic conclusions.

Connected through hyperedge HE-1 (induced by source paper "stochastic control of computation offloading to a helper with a dynamically loaded cpu"):
  with members:
    - "exploiting massive d2d collaboration for energy efficient mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient mobile edge computing in dense cellular networks"
    - "wireless networks for mobile edge computing spatial modeling and latency analysis extended version"
    - "exploiting non causal cpu state information for energy efficient mobile cooperative computing"
    - "joint computation and communication cooperation for mobile edge computing"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"

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.GT(Computer Science and Game Theory)