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: "minimum complexity pursuit"
  Abstract: The fast growing field of compressed sensing is founded on the fact that if a signal is 'simple' and has some 'structure', then it can be reconstructed accurately with far fewer samples than its ambient dimension. Many different plausible structures have been explored in this field, ranging from sparsity to low-rankness and to finite rate of innovation. However, there are important abstract questions that are yet to be answered. For instance, what are the general abstract meanings of 'structure' and 'simplicity'? Do there exist universal algorithms for recovering such simple structured objects from fewer samples than their ambient dimension? In this paper, we aim to address these two questions. Using algorithmic information theory tools such as Kolmogorov complexity, we provide a unified method of describing 'simplicity' and 'structure'. We then explore the performance of an algorithm motivated by Ocam's Razor (called MCP for minimum complexity pursuit) and show that it requires $O(k\log n)$ number of samples to recover a signal, where $k$ and $n$ represent its complexity and ambient dimension, respectively. Finally, we discuss more general classes of signals and provide guarantees on the performance of MCP.

Connected through hyperedge HE-1 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"

Connected through hyperedge HE-2 (induced by source paper "linear time clustering for high dimensional mixtures of gaussian clouds"):
  with members:
    - "tight bounds for learning a mixture of two gaussians"
    - "a two round variant of em for gaussian mixtures"
    - "a concentration theorem for projections"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-3 (induced by source paper "on the gaussianity of kolmogorov complexity of mixing sequences"):
  with members:
    - "entropy rate estimation for markov chains with large state space"
    - "rate distortion and denoising of individual data using kolmogorov complexity"
    - "minimum description length induction bayesianism and kolmogorov complexity"

Connected through hyperedge HE-4 (induced by source paper "approximate message passing with universal denoising"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-5 (induced by source paper "approximate message passing algorithm with universal denoising and gaussian mixture learning"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "compressive imaging via approximate message passing with image denoising"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "mismatched estimation in large linear systems"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"

Connected through hyperedge HE-6 (induced by source paper "compressed sensing via universal denoising and approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "signal estimation with additive error metrics in compressed sensing"
    - "empirical bayes and full bayes for signal estimation"
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "information complexity and estimation"

Connected through hyperedge HE-7 (induced by source paper "universal compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "signal recovery on incoherent manifolds"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "stable image reconstruction using total variation minimization"
    - "minimum complexity pursuit stability analysis"
    - "sampling and recovery of pulse streams"
    - "optimal phase transitions in compressed sensing"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "from compression to compressed sensing"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "minimum complexity pursuit stability analysis"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"

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)