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 "modeling and analysis of scma enhanced d2d and cellular hybrid network"):
  Abstract: Sparse code multiple access (SCMA) has been recently proposed for the future wireless networks, which allows nonorthogonal spectrum resource sharing and enables system overloading. In this paper, we apply SCMA into device-to-device (D2D) communication and cellular hybrid network, targeted at using the overload feature of SCMA to support massive device connectivity and expand network capacity. Particularly, we develop a stochastic geometry-based framework to model and analyze SCMA, considering underlaid and overlaid modes. Based on the results, we analytically compare SCMA with orthogonal frequency-division multiple access (OFDMA) using area spectral efficiency (ASE) and quantify closed-form ASE gain of SCMA over OFDMA. Notably, it is shown that system ASE can be significantly improved using SCMA and the ASE gain scales linearly with the SCMA codeword dimension. Besides, we endow D2D users with an activated probability to balance cross-tier interference in the underlaid mode and derive the optimal activated probability. Meanwhile, we study resource allocation in the overlaid mode and obtain the optimal codebook allocation rule. It is interestingly found that the optimal SCMA codebook allocation rule is independent of cellular network parameters when cellular users are densely deployed. The results are helpful in the implementation of SCMA in the hybrid system.

Includes members:
  Member "a geometrical based throughput bound analysis for device to device communications in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "connectivity of underlay cognitive radio networks with directional antennas")
  Member "a survey on device to device communication in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simultaneous energy harvesting and information transmission in a mimo full duplex system a machine learning based design")
  Member "spectrum sharing for device to device communication in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sense and predict harnessing spatial interference correlation for cognitive radio networks")
  Member "scma codebook design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "design of scma codebooks using differential evolution")
  Member "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "power control for d2d underlaid cellular networks modeling algorithms and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "spectrum sharing between cellular and mobile ad hoc networks transmission capacity trade off":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spatial and temporal analysis of direct communications from static devices to mobile vehicles")

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)