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 "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"):
  Abstract: The heterogeneous cloud radio access network (HCRAN) is a promising paradigm which integrates the advantages of cloud radio access network (C-RAN) and heterogeneous network (HetNet). In this paper, we study the joint congestion control and resource optimization to explore the energy efficiency (EE)-guaranteed tradeoff between throughput utility and delay performance in a downlink slotted H-CRAN. We formulate the considered problem as a stochastic optimization problem, which maximizes the utility of average throughput and maintains the network stability subject to required EE constraint and transmit power consumption constraints by traffic admission control, user association, resource block allocation and power allocation. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed and decomposed into three separate subproblems which can be solved concurrently at each slot. The third mixed-integer nonconvex subproblem is efficiently solved utilizing the continuity relaxation of binary variables and the Lagrange dual decomposition method. Theoretical analysis shows that the proposal can quantitatively control the throughput-delay performance tradeoff with required EE performance. Simulation results consolidate the theoretical analysis and demonstrate the advantages of the proposal from the prospective of queue stability and power consumption.

Includes members:
  Member "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning based mode selection and resource management for green fog radio access networks")
  Member "queue aware energy efficient joint remote radio head activation and beamforming in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resource allocation in cloud radio access networks with device to device communications")
  Member "inter tier interference suppression in heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "contract based interference coordination in heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning based mode selection and resource management for green fog radio access networks")
  Member "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "fronthaul constrained cloud radio access networks insights and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the capacity regions of cloud radio access networks with limited orthogonal fronthaul")
  Member "system architecture and key technologies for 5g heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")

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)