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 "resource allocation for d2d communications with partial channel state information"):
  Abstract: Enhancement of system capacity is one of the objectives of the fifth generation (5G) networks in which device-to-device (D2D) communications is anticipated to play a crucial role. This can be achieved by devising efficient resource allocation strategies for the D2D users. While most of the works in resource allocation assume full knowledge of the channel states, transmitting it in every time slot reduces the system throughput due to extra control overhead and leads to wastage of power. In this paper, we address the problem of D2D resource allocation with partial channel state information (CSI) at the base station (BS) and ensure that the interference from the D2D users do not jeopardize the communications of cellular users (CUs). With partial CSI, existing algorithms determine the Nash equilibrium in a distributed manner, whose inefficiency in maximizing the social utility is well known as the players try to maximize their own utilities. This is the first work in the D2D resource allocation field in which within a game theoretic framework, an optimal D2D resource allocation algorithm is proposed which maximizes the social utility of the D2D players such that a social optimum is attained. Each D2D player with the help of the BS learns to select the optimal action. We consider the channel to exhibit path loss. Next, we consider both a slow and fast fading with CU mobility and propose two heuristic algorithms. We validate the performance of our proposed algorithms through simulation results.

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 "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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)