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 "multi antenna noma for computation offloading in multiuser mobile edge computing systems"):
  Abstract: This paper studies a multiuser mobile edge computing (MEC) system, in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancellation (SIC) to efficiently decode all users' offloaded tasks for remote execution. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS's decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a {\em mixed Boolean convex problem} that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution, and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB based solution, and the convex relaxation based solution achieves a suboptimal performance but with lower implementation complexity.

Includes members:
  Member "wireless powered user cooperative computation in mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading")
  Member "fairness comparison of uplink noma and oma":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random access for massive machine type communications")
  Member "mobile edge computing a survey on architecture and computation offloading":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "uav enabled wireless power transfer trajectory design and energy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "joint offloading and computing optimization in wireless powered mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "delay optimal computation task scheduling for mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis on computation intensive status update in mobile edge computing")
  Member "energy efficient resource allocation for mobile edge computing based augmented reality applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "energy efficient resource allocation for mobile edge computation offloading":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")

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)