human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "stochastic control of computation offloading to a helper with a dynamically loaded cpu"):
  Abstract: Due to densification of wireless networks, there exist abundance of idling computation resources at edge devices. These resources can be scavenged by offloading heavy computation tasks from small IoT devices in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential for a user to intelligently control the amounts of data for offloading and local computing so as to ensure a computation task can be finished in time consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU. Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally-computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper-CPU as Markov chains, the problem of offloading control is converted into a Markov-decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For different cases ranging from zero to large buffers, the low-complexity of the policies overcomes the "curse-of-dimensionality" in DP arising from joint consideration of channel, helper CPU and buffer states.

Includes members:
  Member "exploiting massive d2d collaboration for energy efficient mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing")
  Member "a survey on mobile edge computing the communication perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "energy efficient mobile edge computing in dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "edge computing enabled smart cities a comprehensive survey")
  Member "wireless networks for mobile edge computing spatial modeling and latency analysis extended version":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "survey on multi access edge computing for internet of things realization")
  Member "exploiting non causal cpu state information for energy efficient mobile cooperative computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient resource allocation for mobile edge computing networks with noma completion time and energy minimization")
  Member "joint computation and communication cooperation for mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fog assisted multi user swipt networks local computing or offloading")
  Member "delay optimal computation task scheduling for mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis on computation intensive status update in mobile edge computing")
  Member "less is more participation incentives in d2d enhanced mobile edge computing under infectious ddos attacks":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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.IT(Information Theory)