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: "near gaussian multiple access channel capacity detection and decoding"
  Abstract: We consider the design of low-density parity-check (LDPC) codes with close-to-capacity performance for non-orthogonal interleave division multiple access (IDMA).The degree profile of the LDPC code is optimized using extrinsic information transfer (EXIT) charts to match an IDMA low complexity multi-user detector. Analytical EXIT functions for the IDMA system are derived while verifying that the Gaussian approximation stays valid for a sufficiently large number of users. The resulting LDPC codes are of rather low-rate (e.g., Rc = 0:03 for 32 users) and thus, the practical design of such codes is quite challenging and may, due to the low code-rate, not even yield a performance close to the Gaussian multiple access channel (GMAC) capacity. Adding a serial concatenation with a simple repetition code allows to design matching LDPC codes with a higher rate and, this way, achieving closer to GMAC capacity. Moreover, the designed IDMA system can be flexibly reconfigured to support a wide range of different numbers of users by simply varying the repetition code rate, i.e., without the need for redesigning the LDPC code itself, making it an attractive non-orthogonal multiple access (NOMA) scheme for future wireless communication systems.

Connected through hyperedge HE-1 (induced by source paper "capacity optimality of amp in coded systems"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "a unified framework of state evolution for message passing algorithms"
    - "optimal data detection in large mimo"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "compressive phase retrieval via generalized approximate message passing"
    - "compressed sensing via universal denoising and approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "capacity achieving mimo noma iterative lmmse detection"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "hybrid approximate message passing"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "achievable rates of mimo systems with linear precoding and iterative lmmse detection"
    - "hybrid approximate message passing"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-3 (induced by source paper "spatially coupled ldpc codes and the multiple access channel"):
  with members:
    - "avoiding burst like error patterns in windowed decoding of spatially coupled ldpc codes"
    - "wave like decoding of tail biting spatially coupled ldpc codes through iterative demapping"
    - "spatially coupled ldpc codes constructed from protographs"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "windowed decoding of spatially coupled codes"

Connected through hyperedge HE-4 (induced by source paper "achievable rate region for iterative multi user detection via low cost gaussian approximation"):
  with members:
    - "capacity achieving iterative lmmse detection for mimo noma systems"
    - "achievable rates of mimo systems with linear precoding and iterative lmmse detection"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-5 (induced by source paper "a joint graph based coding scheme for the unsourced random access gaussian channel"):
  with members:
    - "capacity of gaussian many access channels"
    - "the restricted isometry property of subsampled fourier matrices"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "ldpc coded multiuser shaping for the gaussian multiple access channel"):
  with members:
    - "universal codes for the gaussian mac via spatial coupling"
    - "spatially coupled codes over the multiple access channel"

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)