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: "techniques for improving the finite length performance of sparse superposition codes"
  Abstract: Sparse superposition codes are a recent class of codes introduced by Barron and Joseph for efficient communication over the AWGN channel. With an appropriate power allocation, these codes have been shown to be asymptotically capacity-achieving with computationally feasible decoding. However, a direct implementation of the capacity-achieving construction does not give good finite length error performance. In this paper, we consider sparse superposition codes with approximate message passing (AMP) decoding, and describe a variety of techniques to improve their finite length performance. These include an iterative algorithm for SPARC power allocation, guidelines for choosing codebook parameters, and estimating a critical decoding parameter online instead of precomputation. We also show how partial outer codes can be used in conjunction with AMP decoding to obtain a steep waterfall in the error performance curves. We compare the error performance of AMP-decoded sparse superposition codes with coded modulation using LDPC codes from the WiMAX standard.

Connected through hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding"):
  with members:
    - "sparse regression codes"
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"

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"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "sparcs for unsourced random access"
    - "a coupled compressive sensing scheme for unsourced multiple access"

Connected through hyperedge HE-3 (induced by source paper "sparse regression codes"):
  with members:
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "sparcs for unsourced random access"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"):
  with members:
    - "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"
    - "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"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"

Connected through hyperedge HE-5 (induced by source paper "sparcs for unsourced random access"):
  with members:
    - "massive mimo unsourced random access"
    - "sparse signal processing for grant free massive iot connectivity"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "on or many access channels"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "state evolution for approximate message passing with non separable functions"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-6 (induced by source paper "the error probability of sparse superposition codes with approximate message passing decoding"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "phase transitions in sparse pca"
    - "information theoretically optimal sparse pca"
    - "replica analysis and approximate message passing decoder for superposition codes"

Connected through hyperedge HE-7 (induced by source paper "spatially coupled sparse regression codes design and state evolution analysis"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "spatially coupled ldpc codes constructed from protographs"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"

Connected through hyperedge HE-8 (induced by source paper "universal sparse superposition codes with spatial coupling and gamp decoding"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "a compressed sensing approach for distribution matching"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "state evolution for approximate message passing with non separable functions"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "mutual information and optimality of approximate message passing in random linear estimation"

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)