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 "efficient global optimal resource allocation in non orthogonal interference networks"):
  Abstract: Many resource allocation tasks are challenging global (i.e., non-convex) optimization problems. The main issue is that the computational complexity of these problems grows exponentially in the number of variables instead of polynomially as for convex optimization problems. However, often the non-convexity stems only from a subset of variables. Conventional global optimization frameworks like monotonic optimization or DC programming treat all variables as global variables and require complicated, problem specific decomposition approaches to exploit the convexity in some variables. To overcome this challenge, we develop an easy-to-use algorithm that inherently differentiates between convex and non-convex variables, preserving the polynomial complexity in the number of convex variables. Another issue with these widely used frameworks is that they may suffer from severe numerical problems. We discuss this issue in detail and provide a clear motivating example.The solution to this problem is to replace the traditional approach of finding an {\epsilon}-approximate solution by the novel concept of {\epsilon}-essential feasibility. The underlying algorithmic approach is called successive incumbent transcending (SIT) algorithm and builds the foundation of our developed algorithm. A further highlight of our algorithm is that it inherently treats fractional objectives making the use of Dinkelbach's iterative algorithm obsolete. Numerical experiments show a speed-up of five orders of magnitude over state-of-the-art algorithms and almost four orders of magnitude of additional speed-up over Dinkelbach's algorithm for fractional programs.

Includes members:
  Member "optimization of weighted individual energy efficiencies in interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixed monotonic programming for fast global optimization")
  Member "distributed optimization for coordinated beamforming in multicell multigroup multicast systems power minimization and sinr balancing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cloud edge coordinated processing low latency multicasting transmission")
  Member "energy efficient beam coordination strategies with rate dependent processing power":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "energy efficient power control a look at 5g wireless technologies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "optimal achievable rates for interference networks with random codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance of multi cell massive mimo systems with interference decoding")
  Member "resource allocation for energy efficient 3 way relay channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "instantaneous relaying for the 3 way relay channel with circular message exchanges":
    also connected through other hyperedges:
      (none)
  Member "wireless information and power transfer energy efficiency optimization in ofdma systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the performance gain of noma over oma in uplink communication 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)