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: "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding"
  Abstract: In an ultra-dense cloud radio access network (UD-CRAN), a large number of remote radio heads (RRHs), typically employed as simple relay nodes, are distributed in a target area, which could even outnumber their served users. However, one major challenge is that the large amount of information required to be transferred between each RRH and the central processor (CP) for joint signal processing can easily exceed the capacity of the fronthaul links connecting them. This motivates our study in this paper on a new  hybrid  decoding scheme where in addition to quantizing and forwarding the received signals for joint decoding at the CP, i.e.,   forward-and-decode (FaD)  as in the conventional CRAN, the RRHs can locally   decode-and-forward (DaF)  the user messages to save the fronthaul capacity. In particular, we consider the uplink transmission in an orthogonal frequency division multiple access (OFDMA)-based UD-CRAN, where the proposed hybrid decoding is performed on each OFDMA sub-channel (SC). We study a joint optimization of the processing mode selections (DaF or FaD), user-SC assignments and the users’ transmit power allocations over all SCs to maximize their weighted-sum-rate subject to the RRHs’ individual fronthaul capacity constraints and the users’ individual power constraints. Although the problem is nonconvex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. Further, we propose a low-complexity greedy algorithm which is shown to achieve close to the optimal performance under practical setups. Simulation results show the promising throughput gains of the proposed designs with hybrid decoding, compared to the existing schemes that perform either DaF or FaD at all SCs.

Connected through hyperedge HE-1 (induced by source paper "joint uplink and downlink transmissions in user centric ofdma cloud ran"):
  with members:
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "green ofdma resource allocation in cache enabled cran"
    - "why to decouple the uplink and downlink in cellular networks and how to do it"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "coordinated scheduling for the downlink of cloud radio access networks"
    - "what will 5g be"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"

Connected through hyperedge HE-2 (induced by source paper "uplink channel estimation and data transmission in millimeter wave cran with lens antenna arrays"):
  with members:
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "rf lens embedded massive mimo systems fabrication issues and codebook design"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "cost effective millimeter wave communications with lens antenna array"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "optimized uplink transmission in multi antenna c ran with spatial compression and forward"
    - "optimized backhaul compression for uplink cloud radio access network"

Connected through hyperedge HE-3 (induced by source paper "green ofdma resource allocation in cache enabled cran"):
  with members:
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "joint optimization of cloud and edge processing for fog radio access networks"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "wireless communications in the era of big data"
    - "cooperative local caching and file sharing under heterogeneous file preferences"
    - "group sparse beamforming for green cloud ran"
    - "mixed timescale precoding and cache control in cached mimo interference network"

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)