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: "multivariate fronthaul quantization for downlink c ran"
  Abstract: The cloud-radio access network (C-RAN) cellular architecture relies on the transfer of complex baseband signals to and from a central unit (CU) over digital fronthaul links to enable the virtualization of the baseband processing functionalities of distributed radio units (RUs). The standard design of digital fronthauling is based on either scalar quantization or on more sophisticated point-to-point compression techniques operating on baseband signals. Motivated by network-information theoretic results, techniques for fronthaul quantization and compression that improve over point-to-point solutions by allowing for  joint  processing across multiple fronthaul links at the CU have been recently proposed for both the uplink and the downlink. For the downlink, a form of joint compression, known in network information theory as  multivariate compression , was shown to be advantageous under a nonconstructive asymptotic information-theoretic framework. In this paper, instead, the design of a practical symbol-by-symbol fronthaul quantization algorithm that implements the idea of multivariate compression is investigated for the C-RAN downlink. As compared to current standards, the proposed multivariate quantization (MQ) only requires changes in the CU processing while no modification is needed at the RUs. The algorithm is extended to enable the joint optimization of downlink precoding and quantization, reduced-complexity MQ via successive block quantization, and variable-length compression. Numerical results, which include performance evaluations over standard cellular models, demonstrate the advantages of MQ and the merits of a joint optimization with precoding.

Connected through hyperedge HE-1 (induced by source paper "boosting fronthaul capacity global optimization of power sharing for centralized radio access network"):
  with members:
    - "differential evolution algorithm aided turbo channel estimation and multi user detection for g fast systems in the presence of fext"
    - "regularized zero forcing precoding aided adaptive coding and modulation for large scale antenna array based air to air communications"
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "joint user selection and energy minimization for ultra dense multi channel c ran with incomplete csi"
    - "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"

Connected through hyperedge HE-2 (induced by source paper "multi tenant c ran with spectrum pooling downlink optimization under privacy constraints"):
  with members:
    - "optimization of massive full dimensional mimo for positioning and communication"
    - "inter operator base station coordination in spectrum shared millimeter wave cellular networks"
    - "cross layer design for downlink multihop cloud radio access networks with network coding"
    - "joint base station clustering and beamforming for non orthogonal multicast and unicast transmission with backhaul constraints"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"

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)