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: "reed muller sensing matrices and the lasso"
  Abstract: We construct two families of deterministic sensing matrices where the columns are obtained by exponentiating codewords in the quaternary Delsarte-Goethals code $DG(m,r)$. This method of construction results in sensing matrices with low coherence and spectral norm. The first family, which we call Delsarte-Goethals frames, are $2^m$ - dimensional tight frames with redundancy $2^{rm}$. The second family, which we call Delsarte-Goethals sieves, are obtained by subsampling the column vectors in a Delsarte-Goethals frame. Different rows of a Delsarte-Goethals sieve may not be orthogonal, and we present an effective algorithm for identifying all pairs of non-orthogonal rows. The pairs turn out to be duplicate measurements and eliminating them leads to a tight frame. Experimental results suggest that all $DG(m,r)$ sieves with $m\leq 15$ and $r\geq2$ are tight-frames; there are no duplicate rows. For both families of sensing matrices, we measure accuracy of reconstruction (statistical 0-1 loss) and complexity (average reconstruction time) as a function of the sparsity level $k$. Our results show that DG frames and sieves outperform random Gaussian matrices in terms of noiseless and noisy signal recovery using the LASSO.

Connected through hyperedge HE-1 (induced by source paper "joint active user detection and channel estimation in massive access systems exploiting reed muller sequences"):
  with members:
    - "reed muller sequences for 5g grant free massive access"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "compressive random access using a common overloaded control channel"
    - "random access for machine type communication based on bloom filtering"
    - "code expanded random access for machine type communications"

Connected through hyperedge HE-2 (induced by source paper "kerdock codes determine unitary 2 designs"):
  with members:
    - "compressed neighbour discovery using sparse kerdock matrices"
    - "triplespin a generic compact paradigm for fast machine learning computations"
    - "low rank matrix recovery from clifford orbits"

Connected through hyperedge HE-3 (induced by source paper "reed muller sequences for 5g grant free massive access"):
  with members:
    - "towards massive ultra reliable and low latency wireless the art of sending short packets"
    - "what will 5g be"
    - "neighbor discovery for wireless networks via compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "restricted isometry property of random subdictionaries"):
  with members:
    - "random subdictionaries and coherence conditions for sparse signal recovery"
    - "lower bounds for sparse recovery"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "weighted superimposed codes and constrained integer compressed sensing"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-5 (induced by source paper "random subdictionaries and coherence conditions for sparse signal recovery"):
  with members:
    - "lower bounds for sparse recovery"
    - "statistical rip and semi circle distribution of incoherent dictionaries"
    - "weighted superimposed codes and constrained integer compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "compressive demodulation of mutually interfering signals"):
  with members:
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-7 (induced by source paper "deterministic compressed sensing matrices from multiplicative character sequences"):
  with members:
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "deterministic compressed sensing matrices from additive character sequences"
    - "the sparsity gap uncertainty principles proportional to dimension"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"
    - "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"

Connected through hyperedge HE-8 (induced by source paper "deterministic compressed sensing matrices from additive character sequences"):
  with members:
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "the sparsity gap uncertainty principles proportional to dimension"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"

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)