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: "compressive channel estimation and multi user detection in c ran"
  Abstract: This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS) technology to greatly reduce the long identification pilot overhead. A mixed L{2,1}-regularization functional for extended sparse group-sparsity recovery is proposed to exploit the inherently sparse property existing both in user activities and remote radio heads (RRHs) that active users are attached to. Empirical and theoretical guidelines are provided to help choosing tuning parameters which have critical effect on the performance of the penalty functional. To speed up the processing procedure, based on alternating direction method of multipliers and variable splitting strategy, an efficient algorithm is formulated which is guaranteed to be convergent. Numerical results are provided to illustrate the effectiveness of the proposed functional and efficient algorithm.

Connected through hyperedge HE-1 (induced by source paper "compressive massive access for internet of things cloud computing or fog computing"):
  with members:
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "towards massive connectivity support for scalable mmtc communications in 5g networks"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "grant free rateless multiple access a novel massive access scheme for internet of things"

Connected through hyperedge HE-2 (induced by source paper "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"):
  with members:
    - "energy sustainable iot with individual qos constraints through miso swipt multicasting"
    - "sparse representation for wireless communications"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "generalized sparse and low rank optimization for ultra dense networks"

Connected through hyperedge HE-3 (induced by source paper "efficient c ran random access for iot devices learning links via recommendation systems"):
  with members:
    - "a novel alternative to cloud ran for throughput densification coded pilots and fast user packet scheduling at remote radio heads"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "random access protocols for massive mimo"
    - "directional training and fast sector based processing schemes for mmwave channels"
    - "user association and interference management in massive mimo hetnets"
    - "optimal user cell association for massive mimo wireless networks"
    - "analysis of blockage effects on urban cellular networks"

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)