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: "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
  Abstract: This paper determines to within a single measurement the minimum number of measurements required to successfully reconstruct a signal drawn from a Gaussian mixture model in the low-noise regime. The method is to develop upper and lower bounds that are a function of the maximum dimension of the linear subspaces spanned by the Gaussian mixture components. The method not only reveals the existence or absence of a minimum mean-squared error (MMSE) error floor (phase transition) but also provides insight into the MMSE decay via multivariate generalizations of the MMSE dimension and the MMSE power offset, which are a function of the interaction between the geometrical properties of the kernel and the Gaussian mixture. These results apply not only to standard linear random Gaussian measurements but also to linear kernels that minimize the MMSE. It is shown that optimal kernels do not change the number of measurements associated with the MMSE phase transition, rather they affect the sensed power required to achieve a target MMSE in the low-noise regime. Overall, our bounds are tighter and sharper than standard bounds on the minimum number of measurements needed to recover sparse signals associated with a union of subspaces model, as they are not asymptotic in the signal dimension or signal sparsity.

Connected through hyperedge HE-1 (induced by source paper "multilinear compressive learning with prior knowledge"):
  with members:
    - "multilinear compressive learning"
    - "reversible privacy preservation using multi level encryption and compressive sensing"
    - "compressively sensed image recognition"
    - "large scale distributed neural network training through online distillation"
    - "deep mutual learning"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "identity mappings in deep residual networks"
    - "compressed learning a deep neural network approach"

Connected through hyperedge HE-2 (induced by source paper "neumann networks for inverse problems in imaging"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "off the grid model based deep learning o modl"
    - "multi shot sensitivity encoded diffusion mri using model based deep learning modl mussels"
    - "neural ordinary differential equations"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

Connected through hyperedge HE-3 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"):
  with members:
    - "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"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"):
  with members:
    - "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

Connected through hyperedge HE-5 (induced by source paper "bounds on the number of measurements for reliable compressive classification"):
  with members:
    - "mismatch in the classification of linear subspaces sufficient conditions for reliable classification"
    - "learning transformations for clustering and classification"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "sparse subspace clustering algorithm theory and applications"
    - "online adaptive statistical compressed sensing of gaussian mixture models"
    - "model based compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "info greedy sequential adaptive compressed sensing"):
  with members:
    - "information theoretic bounds for adaptive sparse recovery"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "distance based and continuum fano inequalities with applications to statistical estimation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "performance guarantees for adaptive estimation of sparse signals"
    - "communications inspired projection design with application to compressive sensing"
    - "compressive binary search"

Connected through hyperedge HE-7 (induced by source paper "discrimination on the grassmann manifold fundamental limits of subspace classifiers"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "learning robust subspace clustering"
    - "support recovery with sparsely sampled free random matrices"
    - "communications inspired linear discriminant analysis"
    - "on the capacity of large mimo block fading channels"
    - "approximate sparsity pattern recovery information theoretic lower bounds"

Connected through hyperedge HE-8 (induced by source paper "compressed sensing with prior information optimal strategies geometry and bounds"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "off the grid spectral compressed sensing with prior information"
    - "sharp mse bounds for proximal denoising"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "structured compressed sensing from theory to applications"
    - "improved bounds on restricted isometry constants for gaussian matrices"

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)