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: "uplink harq for distributed and cloud ran via separation of control and data planes"
  Abstract: The implementation of uplink HARQ in a Cloud- Radio Access Network RAN (C-RAN) architecture is constrained by the two-way latency on the fronthaul links connecting the Remote Radio Heads (RRHs) with the Baseband Units (BBUs) that perform decoding. To overcome this limitation, this work considers an architecture based on the separation of control and data planes, in which retransmission control decisions are made at the edge of the network, that is, by the RRHs or User Equipments (UEs), while data decoding is carried out remotely at the BBUs. This solution enables low-latency local retransmission decisions to be made at the RRHs or UEs, which are not subject to the fronthaul latency constraints, while at the same time leveraging the decoding capability of the BBUs. #R##N#A system with BBU Hoteling system is considered first in which each RRH has a dedicated BBU in the cloud. For this system, the control-data separation leverages low-latency local feedback from an RRH to drive the HARQ process of a given UE. Throughput and probability of error of this solution are analyzed for the three standard HARQ modes of Type-I, Chase Combining and Incremental Redundancy over a general fading MIMO link. Then, novel user-centric low-latency feedback strategies are proposed and analyzed for the C-RAN architecture, with a single centralized BBU, based on limited "hard" or "soft" local feedback from the RRHs to the UE and on retransmission decisions taken at the UE. The analysis presented in this work allows the optimization of the considered schemes, as well as the investigation of the impact of system parameters such as HARQ protocol type, blocklength and number of antennas on the performance of low-latency local HARQ decisions in BBU Hoteling and C-RAN architectures.

Connected through hyperedge HE-1 (induced by source paper "downlink transmission of short packets framing and control information revisited"):
  with members:
    - "energy efficient packet scheduling with finite blocklength codes convexity analysis and efficient algorithms"
    - "towards massive ultra reliable and low latency wireless the art of sending short packets"
    - "the third order term in the normal approximation for the awgn channel"
    - "finite block length analysis of the incremental redundancy harq"
    - "optimum power control at finite blocklength"
    - "ultra reliable communication in 5g wireless systems"
    - "five disruptive technology directions for 5g"
    - "quasi static multiple antenna fading channels at finite blocklength"

Connected through hyperedge HE-2 (induced by source paper "time asynchronous robust cooperative transmission for the downlink of c ran"):
  with members:
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "on the multiple access channel with asynchronous cognition"
    - "on the compound broadcast channel multiple description coding and interference decoding"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "downlink noncoherent cooperation without transmitter phase alignment"

Connected through hyperedge HE-3 (induced by source paper "cloud radio access network virtualizing wireless access for dense heterogeneous systems"):
  with members:
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "on the effect of fronthaul latency on arq in c ran systems"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "resource allocation optimization for delay sensitive traffic in fronthaul constrained cloud radio access networks"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "parallel and distributed methods for nonconvex optimization part i theory"
    - "backhaul limited asymmetric cooperation for mimo cellular networks via semidefinite relaxation"

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)