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 "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo"):
  Abstract: We put forward a new algorithmic solution to the massive unsourced random access (URA) problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays. This paper makes an observation that spatial signature is key to URA in massive connectivity setups. The proposed scheme relies on a slotted transmission framework but eliminates completely the need for concatenated coding that was introduced in the context of the coupled compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA rely on an inner/outer tree-based encoder/decoder to stitch the slot-wise recovered sequences. This paper takes a different path by harnessing the nature-provided correlations between the slot-wise reconstructed channels of each user in order to put together its decoded sequences. The required slot-wise channel estimates and decoded sequences are first obtained through the powerful hybrid generalized approximate message passing (HyGAMP) algorithm which systematically accommodates the multiantenna-induced group sparsity. Then, a channel correlation-aware clustering framework based on the expectation-maximization (EM) concept is used together with the Hungarian algorithm to find the slot-wise optimal assignment matrices by enforcing two clustering constraints that are very specific to the problem at hand. Stitching is then readily accomplished by associating the decoded sequences to their respective users according to the ensuing assignment matrices. Exhaustive computer simulations reveal that the proposed scheme outperforms by far the only known work from the open literature. More precisely, it will be seen that our scheme can accommodate a very large number of active users with much higher total spectral efficiency while using a remarkably smaller number of receive antennas and achieving a low decoding error probability.

Includes members:
  Member "a joint graph based coding scheme for the unsourced random access gaussian channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on approximate message passing for unsourced access with coded compressed sensing")
  Member "polar coding and random spreading for unsourced multiple access":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "massive mimo unsourced random access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo")
  Member "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior":
    also connected through other hyperedges:
      (none)
  Member "an enhanced decoding algorithm for coded compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on approximate message passing for unsourced access with coded compressed sensing")
  Member "sparcs for unsourced random access":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on approximate message passing for unsourced access with coded compressed sensing")
  Member "improved scaling law for activity detection in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "a coupled compressive sensing scheme for unsourced multiple access":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on approximate message passing for unsourced access with coded compressed sensing")

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)