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: "grant free massive mtc enabled massive mimo a compressive sensing approach"
  Abstract: A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. #R##N#First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. #R##N#Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared to application of the original AMP approach.

Connected through hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "the roadmap to 6g ai empowered wireless networks"
    - "improved scaling law for activity detection in massive mimo systems"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"):
  with members:
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "compressive sensing techniques for next generation wireless communications"

Connected through hyperedge HE-3 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo"):
  with members:
    - "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "massive mimo unsourced random access"
    - "joint active user detection and channel estimation in massive access systems exploiting reed muller sequences"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"

Connected through hyperedge HE-4 (induced by source paper "jointly sparse signal recovery via deep auto encoder and parallel coordinate descent unrolling"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "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"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "deep networks for compressed image sensing"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-5 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access"):
  with members:
    - "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"
    - "faster activity and data detection in massive random access a multi armed bandit approach"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "grant free rateless multiple access a novel massive access scheme for internet of things"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-6 (induced by source paper "massive mimo for internet of things iot connectivity"):
  with members:
    - "massive mimo unsourced random access"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "non stationarities in extra large scale massive mimo"
    - "ubiquitous cell free massive mimo communications"
    - "sparcs for unsourced random access"
    - "management of network slicing in 5g radio access networks functional framework and information models"
    - "massive mimo for ultra reliable communications with constellations for dual coherent noncoherent detection"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"

Connected through hyperedge HE-7 (induced by source paper "analysis and optimization of successful symbol transmission rate for grant free massive access with massive mimo"):
  with members:
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-8 (induced by source paper "free ride on ldpc coded transmission"):
  with members:
    - "accessible capacity of secondary users"

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)