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 enhanced decoding algorithm for coded compressed sensing"
  Abstract: Coded compressed sensing is an algorithmic framework tailored to sparse recovery in very large dimensional spaces. This framework is originally envisioned for the unsourced multiple access channel, a wireless paradigm attuned to machine-type communications. Coded compressed sensing uses a divide-and-conquer approach to break the sparse recovery task into sub-components whose dimensions are amenable to conventional compressed sensing solvers. The recovered fragments are then stitched together using a low complexity decoder. This article introduces an enhanced decoding algorithm for coded compressed sensing where fragment recovery and the stitching process are executed in tandem, passing information between them. This novel scheme leads to gains in performance and a significant reduction in computational complexity. This algorithmic opportunity stems from the realization that the parity structure inherent to coded compressed sensing can be used to dynamically restrict the search space of the subsequent recovery algorithm.

Connected through hyperedge HE-1 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo"):
  with members:
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "polar coding and random spreading for unsourced multiple access"
    - "massive mimo unsourced random access"
    - "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior"
    - "sparcs for unsourced random access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "a coupled compressive sensing scheme for unsourced multiple access"
    - "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 "on approximate message passing for unsourced access with coded compressed sensing"):
  with members:
    - "sparse regression codes"
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "polar coding and random spreading for unsourced multiple access"
    - "sparcs for unsourced random access"
    - "a coupled compressive sensing scheme for unsourced multiple access"
    - "techniques for improving the finite length performance of sparse superposition codes"

Connected through hyperedge HE-3 (induced by source paper "grant free massive random access with a massive mimo receiver"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the restricted isometry property of centered self khatri rao products"
    - "polar coding and random spreading for unsourced multiple access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "sparse activity detection for massive connectivity"

Connected through hyperedge HE-4 (induced by source paper "polar coding and random spreading for unsourced multiple access"):
  with members:
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "efficient scheduling for the massive random access gaussian channel"
    - "sparcs for unsourced random access"

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)