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 "asynchronous mobile edge computation offloading energy efficient resource management"):
  Abstract: Mobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lives, by offloading intensive computation from mobiles to nearby servers such as base stations. In this paper, we study the energy-efficient resource-management policy for the asynchronous MECO system, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is shown to be computed by using the block coordinate decent method. To gain further insights, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to balance the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach, which first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.

Includes members:
  Member "latency optimization for resource allocation in mobile edge computation offloading":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accelerating dnn training in wireless federated edge learning system")
  Member "a survey on mobile edge networks convergence of computing caching and communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "computation rate maximization for wireless powered mobile edge computing with binary computation offloading":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reusing wireless power transfer for backscatter assisted cooperation in wpcn")
  Member "a survey on mobile edge computing the communication perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "joint task offloading scheduling and transmit power allocation for mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "device vs edge computing for mobile services delay aware decision making to minimize power consumption")
  Member "an online optimization framework for distributed fog network formation with minimal latency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "managing fog networks using reinforcement learning based load balancing algorithm")
  Member "exploiting non causal cpu state information for energy efficient mobile cooperative computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient resource allocation for mobile edge computing networks with noma completion time and energy minimization")
  Member "joint computation offloading and resource allocation in cloud based wireless hetnets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint data compression and computation offloading in hierarchical fog cloud systems")

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)