human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "joint power control and fronthaul rate allocation for throughput maximization in ofdma based cloud radio access network"
  Abstract: The performance of cloud radio access network (C-RAN) is constrained by the limited fronthaul link capacity under future heavy data traffic. To tackle this problem, extensive efforts have been devoted to design efficient signal quantization/compression techniques in the fronthaul to maximize the network throughput. However, most of the previous results are based on information-theoretical quantization methods, which are hard to implement due to the extremely high complexity. In this paper, we consider using practical uniform scalar quantization in the uplink communication of an orthogonal frequency division multiple access (OFDMA) based C-RAN system, where the mobile users are assigned with orthogonal sub-carriers for multiple access. In particular, we consider joint wireless power control and fronthaul quantization design over the sub-carriers to maximize the system end-to-end throughput. Efficient algorithms are proposed to solve the joint optimization problem when either information-theoretical or practical fronthaul quantization method is applied. Interestingly, we find that the fronthaul capacity constraints have significant impact to the optimal wireless power control policy. As a result, the joint optimization shows significant performance gain compared with either optimizing wireless power control or fronthaul quantization alone. Besides, we also show that the proposed simple uniform quantization scheme performs very close to the throughput performance upper bound, and in fact overlaps with the upper bound when the fronthaul capacity is sufficiently large. Overall, our results would help reveal practically achievable throughput performance of C-RAN, and lead to more efficient deployment of C-RAN in the next-generation wireless communication systems.

Connected through hyperedge HE-1 (induced by source paper "fronthaul compression and precoding design for c rans over ergodic fading channels"):
  with members:
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "optimal channel training in uplink network mimo systems"

Connected through hyperedge HE-2 (induced by source paper "downlink sinr balancing in c ran under limited fronthaul capacity"):
  with members:
    - "optimized uplink transmission in multi antenna c ran with spatial compression and forward"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "optimized data sharing in multicell mimo with finite backhaul capacity"
    - "cooperative interference management with miso beamforming"

Connected through hyperedge HE-3 (induced by source paper "delay aware uplink fronthaul allocation in cloud radio access networks"):
  with members:
    - "dynamic power control for delay aware device to device communications"
    - "full duplex cloud radio access networks an information theoretic viewpoint"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "multihop backhaul compression for the uplink of cloud radio access networks"
    - "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning"
    - "queue aware dynamic clustering and power allocation for network mimo systems via distributed stochastic learning"
    - "distributive stochastic learning for delay optimal ofdma power and subband allocation"

Connected through hyperedge HE-4 (induced by source paper "optimized uplink transmission in multi antenna c ran with spatial compression and forward"):
  with members:
    - "optimized backhaul compression for uplink cloud radio access network"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "wireless network information flow a deterministic approach"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "distributed mimo receiver achievable rates and upper bounds"

Question: Please tell me which class the center node 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)