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 "submodular functions learnability structure and optimization"):
  Abstract: Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous algorithmic applications. They have been used in many areas, including combinatorial optimization, machine learning, and economics. In this work we study submodular functions from a learning theoretic angle. We provide algorithms for learning submodular functions, as well as lower bounds on their learnability. In doing so, we uncover several novel structural results revealing ways in which submodular functions can be both surprisingly structured and surprisingly unstructured. We provide several concrete implications of our work in other domains including algorithmic game theory and combinatorial optimization. #R##N#At a technical level, this research combines ideas from many areas, including learning theory (distributional learning and PAC-style analyses), combinatorics and optimization (matroids and submodular functions), and pseudorandomness (lossless expander graphs).

Includes members:
  Member "symmetry and approximability of submodular maximization problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "submodular functions are noise stable":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "learning valuation functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "powerset convolutional neural networks")
  Member "a note on concentration of submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "is submodularity testable":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "privately releasing conjunctions and the statistical query barrier":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "submodular maximization by simulated annealing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")
  Member "dependent randomized rounding for matroid polytopes and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint")

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.DS(Data Structures and Algorithms)