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: "multi objective framework for dynamic optimization of ofdma cellular systems"
  Abstract: Green cellular networking has become an important research area in recent years due to environmental and economical concerns. Switching OFF underutilized base stations (BSs) during OFF-peak traffic load conditions is a promising approach to reduce energy consumption in cellular networks. In practice, during initial cell planning, the BS locations and radio access network (RAN) parameters (BS transmit power, antenna height, and antenna tilt) are optimized to meet the basic system design requirements, such as coverage, capacity, overlap, quality of service (QoS), and so on. As these metrics are tightly coupled with each other due to co-channel interference, switching OFF certain BSs may affect the system requirements. Therefore, identifying a subset of large number of BSs, which are to be put into the sleep mode, is a challenging dynamic optimization problem. In this paper, we develop a multi-objective framework for dynamic optimization framework for orthogonal frequency division multiple access-based cellular systems. The objective is to identify the appropriate set of active sectors and RAN parameters that maximize coverage and area spectral efficiency, while minimizing overlap and area power consumption without violating the QoS requirements for a given traffic demand density. The objective functions and constraints are obtained using appropriate analytical models, which capture the traffic characteristics, propagation characteristics (path-loss, shadowing, and small-scale fading), as well as load condition in neighboring cells. A low-complexity evolutionary algorithm is used for identifying the global Pareto optimal solutions at a faster convergence rate. The inter-relationships between the system objectives are studied, and the guidelines are provided to find an appropriate network configuration that provides the best achievable tradeoffs. The results show that using the proposed framework, significant amount of energy saving can be achieved and with a low computational complexity while maintaining good tradeoffs among the other objectives.

Connected through hyperedge HE-1 (induced by source paper "spectral efficiency analysis in presence of correlated gamma lognormal desired and interfering signals"):
  with members:
    - "what will 5g be"

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)