human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cooperative channel estimation for coordinated transmission with limited backhaul"):
  Abstract: Obtaining accurate global channel state information (CSI) at multiple transmitter devices is critical to the performance of many coordinated transmission schemes. Practical CSI local feedback often leads to noisy and partial CSI estimates at each transmitter. With rate-limited bi-directional backhaul, transmitters have the opportunity to exchange few CSI-related bits to establish global channel state information at transmitter (CSIT). This work investigates possible strategies towards this goal. We propose a novel decentralized algorithm that produces minimum mean square error (MMSE)-optimal global channel estimates at each device from combining local feedback and information exchanged through backhauls. The method adapts to arbitrary initial information topologies and feedback noise statistics and can do that with a combination of closed-form and convex approaches. Simulations for coordinated multi-point (CoMP) transmission systems with two or three transmitters exhibit the advantage of the proposed algorithm over conventional CSI exchange mechanisms when the coordination backhauls are limited.

Includes members:
  Member "csit sharing over finite capacity backhaul for spatial interference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the benefits of edge caching for mimo interference alignment")
  Member "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "covariance aided csi acquisition with non orthogonal pilots in massive mimo a large system performance analysis")
  Member "robust monotonic optimization framework for multicell miso systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust resource allocation for pd noma based miso heterogeneous networks with comp technology")
  Member "uplink comp under a constrained backhaul and imperfect channel knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "making cell free massive mimo competitive with mmse processing and centralized implementation")
  Member "k means has polynomial smoothed complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of ward s method")
  Member "adaptive limited feedback for sum rate maximizing beamforming in cooperative multicell systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rate splitting for multi antenna downlink precoder design and practical implementation")
  Member "distributed compression for the uplink of a backhaul constrained coordinated cellular network":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)