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 wireless fronthaul and ofdma resource allocation in ultra dense cran"
  Abstract: Ultra-dense (UD) wireless networks and cloud radio access networks (CRAN) are two promising network architectures for the emerging 5G wireless communication systems. By jointly employing UD network with CRAN, a new appealing network solution is proposed, termed UD-CRAN, which can achieve ultra-high-throughput wireless connectivity. In UD-CRAN, a wireless fronthaul is practically favorable to connect a central processor (CP) with distributed remote radio heads (RRHs), due to its lower cost and higher flexibility to deploy. As the wireless fronthaul capacity is limited, it motivates considering the downlink transmission in a UD-CRAN where the wireless fronthaul is shared among the RRHs via time division multiple access~(TDMA), while the RRHs jointly transmit to the users via orthogonal frequency division multiple access (OFDMA). The joint resource allocation over the TDMA-based wireless fronthaul and OFDMA-based wireless transmission is investigated to maximize the weighted sum rate of all users. Although the problem is combinatorial and non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. To further reduce the complexity, we also propose a greedy search based heuristic, which achieves close to optimal performance under practical setups. Finally, we compare the performance of proposed solutions with other benchmark schemes by simulations, which show significant throughput gains of the proposed joint resource allocation approach.

Connected through hyperedge HE-1 (induced by source paper "echo state networks for proactive caching in cloud based radio access networks with mobile users"):
  with members:
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "optimal fronthaul quantization for cloud radio positioning"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "octopus a cooperative hierarchical caching strategy for cloud radio access networks"
    - "mobility aware caching for content centric wireless networks modeling and methodology"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"
    - "caching improvement using adaptive user clustering"

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)