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: "generalized compression strategy for the downlink cloud radio access network"
  Abstract: This paper studies the downlink of a cloud radio access network (C-RAN) in which a centralized processor (CP) communicates with mobile users through base stations (BSs) that are connected to the CP via finite-capacity fronthaul links. Information theoretically, the downlink of a C-RAN is modeled as a two-hop broadcast-relay network. Among the various transmission and relaying strategies for such model, this paper focuses on the compression strategy, in which the CP centrally encodes the signals to be broadcasted jointly by the BSs, then compresses and sends these signals to the BSs through the fronthaul links. This paper characterizes an achievable rate region for a generalized compression strategy with Marton's multicoding for broadcasting and multivariate compression for fronthaul transmission. We then compare this rate region with the distributed decode-forward (DDF) scheme, which achieves the capacity of the general relay networks to within a constant gap, and show that the difference lies in that DDF performs Marton's multicoding and multivariate compression jointly as opposed to successively as in the compression strategy. A main result of this paper is that under the assumption that the fronthaul links are subject to a \emph{sum} capacity constraint this difference is immaterial, so the successive encoding based compression strategy can already achieve the capacity region of the C-RAN to within a constant gap, where the gap is independent of the channel parameters and the power constraints at the BSs. For the special case of the Gaussian network, we further establish that under individual fronthaul constraints, the compression strategy achieves to within a constant gap to the \emph{sum} capacity of the C-RAN.

Connected through hyperedge HE-1 (induced by source paper "on the capacity regions of cloud radio access networks with limited orthogonal fronthaul"):
  with members:
    - "integer forcing architectures for uplink cloud radio access networks"
    - "the capacity of the relay channel solution to cover s problem in the gaussian case"
    - "on the capacity of cloud radio access networks with oblivious relaying"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "an uplink downlink duality for cloud radio access network"
    - "on achievability for downlink cloud radio access networks with base station cooperation"
    - "capacity bounds for diamond networks with an orthogonal broadcast channel"

Connected through hyperedge HE-2 (induced by source paper "hybrid data sharing and compression strategy for downlink cloud radio access network"):
  with members:
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "distributed decode forward for relay networks"
    - "backhaul limited asymmetric cooperation for mimo cellular networks via semidefinite relaxation"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "group sparse beamforming for green cloud ran"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "linear precoding and equalization for network mimo with partial cooperation"
    - "compute and forward strategies for cooperative distributed antenna 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)