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: "optimal fronthaul quantization for cloud radio positioning"
  Abstract: Wireless positioning systems that are implemented by means of a cloud radio access network (C-RAN) may provide cost-effective solutions, particularly for indoor localization. In a C-RAN, baseband processing, including localization, is carried out at a centralized control unit (CU) based on quantized baseband signals received from the radio units (RUs) over finite-capacity fronthaul links. In this paper, the problem of maximizing the localization accuracy over fronthaul quantization/compression is formulated by adopting the Cramer–Rao bound (CRB) on the localization accuracy as the performance metric of interest and information-theoretic bounds on the compression rate. The analysis explicitly accounts for the uncertainty of parameters at the CU via a robust, or worst-case, optimization formulation. The proposed algorithm leverages the Charnes–Cooper transformation and difference-of-convex (DC) programming and is validated via numerical results.

Connected through hyperedge HE-1 (induced by source paper "optimization of massive full dimensional mimo for positioning and communication"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "a pessimistic approximation for the fisher information measure"
    - "sectoring in multi cell massive mimo systems"
    - "layered downlink precoding for c ran systems with full dimensional mimo"
    - "positioning for the internet of things a 3gpp perspective"
    - "downlink achievable rate analysis in massive mimo systems with one bit dacs"
    - "parallel and distributed methods for nonconvex optimization part ii applications"
    - "quantized precoding for massive mu mimo"

Connected through hyperedge HE-2 (induced by source paper "recent advances in cloud radio access networks system architectures key techniques and open issues"):
  with members:
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "are heterogeneous cloud based radio access networks cost effective"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"
    - "cooperative interference mitigation and handover management for heterogeneous cloud small cell networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "distributed fronthaul compression and joint signal recovery in cloud ran"

Connected through hyperedge HE-3 (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"
    - "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"
    - "joint wireless fronthaul and ofdma resource allocation in ultra dense cran"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "positioning via direct localization in c ran systems"):
  with members:
    - "beamforming design for joint localization and data transmission in distributed antenna system"

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)