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: "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
  Abstract: Compressed Sensing aims to capture attributes of k-sparse signals using very few measurements. In the standard compressed sensing paradigm, the N × C measurement matrix ? is required to act as a near isometry on the set of all k-sparse signals (restricted isometry property or RIP). Although it is known that certain probabilistic processes generate N × C matrices that satisfy RIP with high probability, there is no practical algorithm for verifying whether a given sensing matrix ? has this property, crucial for the feasibility of the standard recovery algorithms. In contrast, this paper provides simple criteria that guarantee that a deterministic sensing matrix satisfying these criteria acts as a near isometry on an overwhelming majority of k-sparse signals; in particular, most such signals have a unique representation in the measurement domain. Probability still plays a critical role, but it enters the signal model rather than the construction of the sensing matrix. An essential element in our construction is that we require the columns of the sensing matrix to form a group under pointwise multiplication. The construction allows recovery methods for which the expected performance is sub-linear in C, and only quadratic in N, as compared to the super-linear complexity in C of the Basis Pursuit or Matching Pursuit algorithms; the focus on expected performance is more typical of mainstream signal processing than the worst case analysis that prevails in standard compressed sensing. Our framework encompasses many families of deterministic sensing matrices, including those formed from discrete chirps, Delsarte-Goethals codes, and extended BCH codes.

Connected through hyperedge HE-1 (induced by source paper "frame moments and welch bound with erasures"):
  with members:
    - "random subsets of structured deterministic frames have manova spectra"
    - "analog coding of a source with erasures"
    - "sampling versus random binning for multiple descriptions of a bandlimited source"

Connected through hyperedge HE-2 (induced by source paper "randomness and isometries in echo state networks and compressed sensing"):
  with members:
    - "optoelectronic reservoir computing"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "compressed neighbour discovery using sparse kerdock matrices"):
  with members:
    - "virtual full duplex wireless communication via rapid on off division duplex"

Connected through hyperedge HE-4 (induced by source paper "random subsets of structured deterministic frames have manova spectra"):
  with members:
    - "analog coding of a source with erasures"
    - "the restricted isometry property of subsampled fourier matrices"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "the restricted isometry property for time frequency structured random matrices"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"
    - "asymptotic behaviour of random vandermonde matrices with entries on the unit circle"

Connected through hyperedge HE-5 (induced by source paper "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"):
  with members:
    - "two stage subspace constrained precoding in massive mimo cellular systems"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"
    - "effects of channel aging in massive mimo systems"

Connected through hyperedge HE-6 (induced by source paper "unknown sparsity in compressed sensing denoising and inference"):
  with members:
    - "rounding sum of squares relaxations"
    - "correcting camera shake by incremental sparse approximation"
    - "estimating unknown sparsity in compressed sensing"
    - "ldpc codes for compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the stability of low rank matrix reconstruction a constrained singular value view"

Connected through hyperedge HE-7 (induced by source paper "nonadaptive group testing with random set of defectives"):
  with members:
    - "poisson group testing a probabilistic model for boolean compressed sensing"
    - "grotesque noisy group testing quick and efficient"
    - "on almost disjunct matrices for group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-8 (induced by source paper "a generalized ldpc framework for robust and sublinear compressive sensing"):
  with members:
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "efficient sketches for the set query problem"

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)