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 signal processing with linear and non linear observations a unified shannon theoretic approach"
  Abstract: In this work we derive fundamental limits for many linear and non-linear sparse signal processing models including linear and non-linear sparse regression, group testing, multivariate regression and problems with missing features. In general, sparse signal processing problems can be characterized in terms of the following Markovian property. We are given a set of $N$ variables $X_1,X_2,\ldots,X_N$, and there is an unknown subset of variables $S \subset \{1,2,\ldots, N\}$ that are \emph{relevant} for predicting outcomes/outputs $Y$. More specifically, when $Y$ is conditioned on $\{X_n\}_{n\in S}$ it is conditionally independent of the other variables, $\{X_n\}_{n \not \in S}$. Our goal is to identify the set $S$ from samples of the variables $X$ and the associated outcomes $Y$. We characterize this problem as a version of the noisy channel coding problem. Using asymptotic information theoretic analyses, we establish mutual information formulas that provide sufficient and necessary conditions on the number of samples required to successfully recover the salient variables. These mutual information expressions unify conditions for both linear and non-linear observations. We then compute sample complexity bounds for the aforementioned models, based on the mutual information expressions in order to demonstrate the applicability and flexibility of our results in general sparse signal processing models.

Connected through hyperedge HE-1 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "lower bounds for compressed sensing with generative models"
    - "solving inverse problems via auto encoders"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "the universal approximation power of finite width deep relu networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-2 (induced by source paper "performance of group testing algorithms with near constant tests per item"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "partition information and its transmission over boolean multi access channels"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-3 (induced by source paper "capacity of gaussian many access channels"):
  with members:
    - "on or many access channels"
    - "sparse ofdm a compressive sensing approach to asynchronous neighbor discovery"
    - "identifying randomly activated users via sign compute resolve on graphs"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"
    - "many access for small packets based on precoding and sparsity aware recovery"
    - "many access channels the gaussian case with random user activities"
    - "many broadcast channels definition and capacity in the degraded case"
    - "error performance of channel coding in random access communication"

Connected through hyperedge HE-4 (induced by source paper "limits on support recovery with probabilistic models an information theoretic framework"):
  with members:
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "signal estimation with additive error metrics in compressed sensing"
    - "on the dispersions of three network information theory problems"
    - "group testing algorithms bounds and simulations"
    - "living on the edge phase transitions in convex programs with random data"
    - "the capacity of adaptive group testing"
    - "support recovery with sparsely sampled free random matrices"

Connected through hyperedge HE-5 (induced by source paper "identifying outliers in large matrices via randomized adaptive compressive sampling"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "compressive principal component pursuit"
    - "lower bounds for adaptive sparse recovery"
    - "compressive binary search"
    - "sketched svd recovering spectral features from compressive measurements"
    - "algorithms and hardness for robust subspace recovery"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-6 (induced by source paper "discrimination on the grassmann manifold fundamental limits of subspace classifiers"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "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-7 (induced by source paper "sparse recovery with linear and nonlinear observations dependent and noisy data"):
  with members:
    - "lectures on designing screening experiments"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "optimal phase transitions in compressed sensing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "information theoretic bounds for compressed sensing"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-8 (induced by source paper "group testing algorithms bounds and simulations"):
  with members:
    - "non adaptive group testing based on sparse pooling graphs"
    - "the capacity of adaptive group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "adaptive group testing as channel coding with feedback"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"

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)