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: "measurement bounds for sparse signal ensembles via graphical models"
  Abstract: In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework by defining ensemble sparsity models, allowing a correlated ensemble of sparse signals to be jointly recovered from a collection of separately acquired compressive measurements. In this paper, we introduce a framework for modeling sparse signal ensembles that quantifies the intra- and inter-signal dependencies within and among the signals. This framework is based on a novel bipartite graph representation that links the sparse signal coefficients with the measurements obtained for each signal. Using our framework, we provide fundamental bounds on the number of noiseless measurements that each sensor must collect to ensure that the signals are jointly recoverable.

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"
    - "sparse multinomial logistic regression via approximate message passing"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "compressed anomaly detection with multiple mixed observations"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "compressed hypothesis testing to mix or not to mix"
    - "on the limits of sequential testing in high dimensions"
    - "statistical compressed sensing of gaussian mixture models"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "iterative hard thresholding for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "performance limits for noisy multimeasurement vector problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "phase transitions in sparse pca"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "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-5 (induced by source paper "improved support recovery guarantees for the group lasso with applications to structural health monitoring"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on block coherence of frames"
    - "the degrees of freedom of the group lasso for a general design"
    - "subspace methods for joint sparse recovery"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "recovery of block sparse representations from noisy observations via orthogonal matching pursuit"
    - "structured compressed sensing from theory to applications"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-6 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "multi terminal probabilistic compressed sensing"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "exact recovery conditions for sparse representations with partial support information"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"

Connected through hyperedge HE-7 (induced by source paper "performance bounds for sparse signal reconstruction with multiple side information"):
  with members:
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "adaptive rate compressive sensing using side information"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressed sensing with side information geometrical interpretation and performance bounds"
    - "robust pca with partial subspace knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"

Connected through hyperedge HE-8 (induced by source paper "distributed compressive sensing performance analysis with diverse signal ensembles"):
  with members:
    - "on the exact recovery condition of simultaneous orthogonal matching pursuit"
    - "living on the edge phase transitions in convex programs with random data"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "orthogonal matching pursuit with replacement"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "sparse recovery from combined fusion frame measurements"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

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)