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: "necessary and sufficient conditions for sparsity pattern recovery"
  Abstract: The paper considers the problem of detecting the sparsity pattern of a k -sparse vector in \BBR n from m random noisy measurements. A new necessary condition on the number of measurements for asymptotically reliable detection with maximum-likelihood (ML) estimation and Gaussian measurement matrices is derived. This necessary condition for ML detection is compared against a sufficient condition for simple maximum correlation (MC) or thresholding algorithms. The analysis shows that the gap between thresholding and ML can be described by a simple expression in terms of the total signal-to-noise ratio (SNR), with the gap growing with increasing SNR. Thresholding is also compared against the more sophisticated Lasso and orthogonal matching pursuit (OMP) methods. At high SNRs, it is shown that the gap between Lasso and OMP over thresholding is described by the range of powers of the nonzero component values of the unknown signals. Specifically, the key benefit of Lasso and OMP over thresholding is the ability of Lasso and OMP to detect signals with relatively small components.

Connected through hyperedge HE-1 (induced by source paper "harmless interpolation of noisy data in regression"):
  with members:
    - "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"
    - "two models of double descent for weak features"
    - "a new look at an old problem a universal learning approach to linear regression"
    - "kernel and deep regimes in overparametrized models"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-2 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior"):
  with members:
    - "inference under information constraints i lower bounds from chi square contraction"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "joint design of measurement matrix and sparse support recovery method via deep auto encoder"):
  with members:
    - "deep compressed sensing"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deep learning for massive mimo csi feedback"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "estimation of sparsity via simple measurements"):
  with members:
    - "nonadaptive group testing with random set of defectives"
    - "compressed sensing without sparsity assumptions"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "linear boolean classification coding and the critical problem"

Connected through hyperedge HE-5 (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"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"

Connected through hyperedge HE-6 (induced by source paper "an information theoretic study for noisy compressed sensing with joint sparsity model 2"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "subspace recovery from structured union of subspaces"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "information theoretic bounds for compressed sensing"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-7 (induced by source paper "compressive sensing for millimeter wave antenna array diagnosis"):
  with members:
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-8 (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"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"

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)