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: "a stability result for sparse convolutions"
  Abstract: We will establish in this note a stability result for sparse convolutions on torsion-free additive (discrete) abelian groups. Sparse convolutions on torsion-free groups are free of cancellations and hence admit stability, i.e. injectivity with a universal lower bound $\alpha=\alpha(s,f)$, only depending on the cardinality $s$ and $f$ of the supports of both input sequences. More precisely, we show that $\alpha$ depends only on $s$ and $f$ and not on the ambient dimension. This statement follows from a reduction argument which involves a compression into a small set preserving the additive structure of the supports.

Connected through hyperedge HE-1 (induced by source paper "sparse signal processing concepts for efficient 5g system design"):
  with members:
    - "self calibration and biconvex compressive sensing"
    - "robust iterative interference alignment for cellular networks with limited feedback"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "the achievable performance of convex demixing"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "five disruptive technology directions for 5g"
    - "the papr problem in ofdm transmission new directions for a long lasting problem"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-2 (induced by source paper "self calibration and biconvex compressive sensing"):
  with members:
    - "sparse signal processing concepts for efficient 5g system design"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "exact joint sparse frequency recovery via optimization methods"
    - "identifiability scaling laws in bilinear inverse problems"
    - "blind calibration in compressed sensing using message passing algorithms"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "sparse signal recovery from quadratic measurements via convex programming"

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)