human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mathematics of sparsity and entropy axioms core functions and sparse recovery"):
  Abstract: Sparsity and entropy are pillar notions of modern theories in signal processing and information theory. However, there is no clear consensus among scientists on the characterization of these notions. Previous efforts have contributed to understand individually sparsity or entropy from specific research interests. This paper proposes a mathematical formalism, a joint axiomatic characterization, which contributes to comprehend (the beauty of) sparsity and entropy. The paper gathers and introduces inherent and first principles criteria as axioms and attributes that jointly characterize sparsity and entropy. The proposed set of axioms is constructive and allows to derive simple or core functions and further generalizations. Core sparsity generalizes the Hoyer measure, Gini index and pq-means. Core entropy generalizes the R´ enyi entropy and Tsallis entropy, both of which generalize Shannon entropy. Finally, core functions are successfully applied to compressed sensing and to minimum entropy given sample moments. More importantly, the (simplest) core sparsity adds theoretical support to the ‘1-minimization approach in compressed sensing.

Includes members:
  Member "estimating unknown sparsity in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "an axiomatic theory of fairness in network resource allocation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair resource allocation in federated learning")
  Member "comparing measures of sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "concise explanations of neural networks using adversarial training")
  Member "non negative matrix factorization with sparseness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling overlapping beliefs by structured matrix factorization")

Question: Please tell me which class the source paper 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)