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: "sparse multinomial logistic regression via approximate message passing"
  Abstract: For the problem of multi class linear classification and feature selection, we propose approximate message passing approaches to sparse multinomial logistic regression (MLR). First, we propose two algorithms based on the Hybrid Generalized Approximate Message Passing framework: one finds the maximum a posteriori linear classifier and the other finds an approximation of the test-error-rate minimizing linear classifier. Then we design computationally simplified variants of these two algorithms. Next, we detail methods to tune the hyperparameters of their assumed statistical models using Stein's unbiased risk estimate and expectation-maximization, respectively. Finally, using both synthetic and real-world datasets, we demonstrate improved error-rate and runtime performance relative to existing state-of-the-art approaches to sparse MLR.

Connected through hyperedge HE-1 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "performance analysis of approximate message passing for distributed compressed sensing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "signal estimation with additive error metrics in compressed sensing"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "performance regions in compressed sensing from noisy measurements"
    - "subspace methods for joint sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"
    - "parametric bilinear generalized approximate message passing"

Connected through hyperedge HE-3 (induced by source paper "sketched clustering via hybrid approximate message passing"):
  with members:
    - "quantized compressive k means"
    - "hybrid approximate message passing"
    - "sketching for large scale learning of mixture models"
    - "compressive k means"
    - "expectation maximization gaussian mixture approximate message passing"
    - "hybrid approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-4 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"
    - "parametric bilinear generalized approximate message passing"

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)