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 "when distributed outperforms centralized scheduling in d2d enabled cellular networks"):
  Abstract: Device-to-device (D2D) communications is a promising technique for improving the efficiency of 5G networks. Employing channel adaptive resource allocation can yield to a large enhancement in almost any performance metric of D2D communications (e.g. Energy Efficiency). In this paper, we show that minimizing the users transmission power while maintaining predefined throughput constraint can be performed by simply adapting existing LTE CSI feedback. Thus, we propose a centralized scheduling that requires the knowledge of D2D links' Channel State Information (CSI) at the base station level. However, this CSI reporting suffers from the limited number of resources available for feedback transmission. Alternately, we propose a distributed algorithm for resource allocation that benefits from the users' knowledge of their local CSI in order to minimize the users' transmission power while maintaining predefined throughput constraints. The key idea is that users compute their local performance metrics (e.g. energy efficiency) and then use a new signaling mechanism to share these values between each other. Under some condition, the performance of this distributed algorithm achieves that of the \textit{ideal} scheduling (i.e. with a global CSI knowledge of all the D2D links). We describe how these algorithms can be simply integrated to existing cellular networks. Furthermore, numerical results are presented to corroborate our claims and demonstrate the gain that the proposed scheduling algorithms bring to cellular networks.

Includes members:
  Member "hybrid centralized distributed resource allocation for device to device communication underlaying cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sector based radio resource allocation sbrra algorithm for better quality of service and experience in device to device d2d communication")
  Member "energy efficient resource allocation for device to device underlay communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed artificial intelligence solution for d2d communication in 5g networks")
  Member "performance analysis of network assisted two hop d2d communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a dynamic and incentive policy for selecting d2d mobile relays")
  Member "distributed resource allocation in device to device enhanced cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a cascaded channel power allocation for d2d underlaid cellular networks using matching theory")
  Member "resource optimization in device to device cellular systems using time frequency hopping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interference prediction in wireless networks stochastic geometry meets recursive filtering")
  Member "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games")

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)