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: "is submodularity testable"
  Abstract: We initiate the study of property testing of submodularity on the boolean hypercube. Submodular functions come up in a variety of applications in combinatorial optimization. For a vast range of algorithms, the existence of an oracle to a submodular function is assumed. But how does one check if this oracle indeed represents a submodular function? #R##N#Consider a function f:{0,1}^n \rightarrow R. The distance to submodularity is the minimum fraction of values of $f$ that need to be modified to make f submodular. If this distance is more than epsilon > 0, then we say that f is epsilon-far from being submodular. The aim is to have an efficient procedure that, given input f that is epsilon-far from being submodular, certifies that f is not submodular. We analyze a very natural tester for this problem, and prove that it runs in subexponential time. This gives the first non-trivial tester for submodularity. On the other hand, we prove an interesting lower bound (that is, unfortunately, quite far from the upper bound) suggesting that this tester cannot be very efficient in terms of epsilon. This involves non-trivial examples of functions which are far from submodular and yet do not exhibit too many local violations. #R##N#We also provide some constructions indicating the difficulty in designing a tester for submodularity. We construct a partial function defined on exponentially many points that cannot be extended to a submodular function, but any strict subset of these values can be extended to a submodular function.

Connected through hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-2 (induced by source paper "testing convexity of functions over finite domains"):
  with members:
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"

Connected through hyperedge HE-3 (induced by source paper "supermodular locality sensitive hashes"):
  with members:
    - "finding the submodularity hidden in symmetric difference"
    - "testing submodularity and other properties of valuation functions"
    - "slack and margin rescaling as convex extensions of supermodular functions"
    - "submodular hamming metrics"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-4 (induced by source paper "testing submodularity and other properties of valuation functions"):
  with members:
    - "analysis of boolean functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "learning coverage functions and private release of marginals"
    - "partially symmetric functions are efficiently isomorphism testable"
    - "learning valuation functions"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-5 (induced by source paper "tight bounds on low degree spectral concentration of submodular and xos functions"):
  with members:
    - "analysis of boolean functions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "nearly tight bounds on ell_1 approximation of self bounding functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "on the sum of l1 influences"
    - "learning pseudo boolean k dnf and submodular functions"

Connected through hyperedge HE-6 (induced by source paper "a totally unimodular view of structured sparsity"):
  with members:
    - "provable submodular minimization using wolfe s algorithm"
    - "group sparse model selection hardness and relaxations"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "learning with submodular functions a convex optimization perspective"
    - "structured sparsity inducing norms through submodular functions"
    - "model based compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "optimal bounds on approximation of submodular and xos functions by juntas"):
  with members:
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "dnf sparsification and a faster deterministic counting algorithm"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "learning valuation functions"
    - "a note on concentration of submodular functions"
    - "submodular functions learnability structure and optimization"

Connected through hyperedge HE-8 (induced by source paper "on the approximation of submodular functions"):
  with members:
    - "a truthful randomized mechanism for combinatorial public projects via convex optimization"
    - "secondary spectrum auctions for symmetric and submodular bidders"
    - "submodular approximation sampling based algorithms and lower bounds"

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.DS(Data Structures and Algorithms)