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: "combining geometry and combinatorics a unified approach to sparse signal recovery"
  Abstract: There are two main algorithmic approaches to sparse signal recovery: geometric and combinatorial. The geometric approach starts with a geometric constraint on the measurement matrix and then uses linear programming to decode information about the signal from its measurements. The combinatorial approach constructs the measurement matrix and a combinatorial decoding algorithm to match. We present a unified approach to these two classes of sparse signal recovery algorithms. #R##N#The unifying elements are the adjacency matrices of high-quality unbalanced expanders. We generalize the notion of Restricted Isometry Property (RIP), crucial to compressed sensing results for signal recovery, from the Euclidean norm to the l_p norm for p about 1, and then show that unbalanced expanders are essentially equivalent to RIP-p matrices. #R##N#From known deterministic constructions for such matrices, we obtain new deterministic measurement matrix constructions and algorithms for signal recovery which, compared to previous deterministic algorithms, are superior in either the number of measurements or in noise tolerance.

Connected through hyperedge HE-1 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding"):
  with members:
    - "optimal non adaptive group testing"
    - "group testing an information theory perspective"
    - "simple codes and sparse recovery with fast decoding"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "cross sender bit mixing coding"
    - "on the optimality of the kautz singleton construction in probabilistic group testing"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "inverting deep generative models one layer at a time"):
  with members:
    - "asymptotics of map inference in deep networks"
    - "rate optimal denoising with deep neural networks"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "random mesh projectors for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "sunlayer stable denoising with generative networks"
    - "phase retrieval under a generative prior"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"

Connected through hyperedge HE-3 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "stable robust and super fast reconstruction of tensors using multi way projections"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "low rank approximation and regression in input sparsity time"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-4 (induced by source paper "simple codes and sparse recovery with fast decoding"):
  with members:
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "for all sparse recovery in near optimal time"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "exact learning from an honest teacher that answers membership queries"):
  with members:
    - "exact learning of juntas from membership queries"
    - "almost optimal cover free families"
    - "progress on polynomial identity testing ii"
    - "linear time constructions of some d restriction problems"
    - "on exact learning monotone dnf from membership queries"
    - "learning boolean halfspaces with small weights from membership queries"
    - "bounds on the rate of disjunctive codes"
    - "efficient computation of representative sets with applications in parameterized and exact algorithms"

Connected through hyperedge HE-6 (induced by source paper "total variation minimization in compressed sensing"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "learning without concentration"
    - "structured random measurements in signal processing"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "guarantees of total variation minimization for signal recovery"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-7 (induced by source paper "a fast noniterative algorithm for compressive sensing using binary measurement matrices"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressed sensing matrices from fourier matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

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.DM(Discrete Mathematics)