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: "an uplink downlink duality for cloud radio access network"
  Abstract: Uplink-downlink duality refers to the fact that the Gaussian broadcast channel has the same capacity region as the dual Gaussian multiple-access channel under the same sumpower constraint. This paper investigates a similar duality relationship between the uplink and downlink of a cloud radio access network (C-RAN), where a central processor (CP) cooperatively serves multiple mobile users through multiple remote radio heads (RRHs) connected to the CP with finite-capacity fronthaul links. The uplink of such a C-RAN model corresponds to a multipleaccess relay channel; the downlink corresponds to a broadcast relay channel. This paper considers compression-based relay strategies in both uplink and downlink C-RAN, where the quantization noise levels are functions of the fronthaul link capacities. If the fronthaul capacities are infinite, the conventional uplinkdownlink duality applies. The main result of this paper is that even when the fronthaul capacities are finite, duality continues to hold for the case where independent compression is applied across each RRH in the sense that when the transmission and compression designs are jointly optimized, the achievable rate regions of the uplink and downlink remain identical under the same sum-power and individual fronthaul capacity constraints. As an application of the duality result, the power minimization problem in downlink C-RAN can be efficiently solved based on its uplink counterpart.

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"
    - "generalized compression strategy for the downlink cloud radio access network"
    - "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"
    - "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 "fog massive mimo a user centric seamless hot spot architecture"):
  with members:
    - "a novel alternative to cloud ran for throughput densification coded pilots and fast user packet scheduling at remote radio heads"
    - "realizing the tactile internet haptic communications over next generation 5g cellular networks"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "ergodic spectral efficiency in mimo cellular networks"
    - "unique coverage in boolean models"
    - "system level performance evaluation of lte v2x network"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "directional training and fast sector based processing schemes for mmwave channels"

Connected through hyperedge HE-3 (induced by source paper "generalized compression strategy for the downlink cloud radio access network"):
  with members:
    - "hybrid data sharing and compression strategy for downlink cloud radio access network"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "on achievability for downlink cloud radio access networks with base station cooperation"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "distributed decode forward for relay networks"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"

Connected through hyperedge HE-4 (induced by source paper "channel diagonalization for cloud radio access"):
  with members:
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"

Connected through hyperedge HE-5 (induced by source paper "on the capacity of cloud radio access networks with oblivious relaying"):
  with members:
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "opening the black box of deep neural networks via information"
    - "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"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "deep learning and the information bottleneck principle"
    - "on discrete alphabets for the two user gaussian interference channel with one receiver lacking knowledge of the interfering codebook"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"

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)