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: "tight bounds on low degree spectral concentration of submodular and xos functions"
  Abstract: Submodular and fractionally subadditive (or equivalently XOS) functions play a fundamental role in combinatorial optimization, algorithmic game theory and machine learning. Motivated by learnability of these classes of functions from random examples, we consider the question of how well such functions can be approximated by low-degree polynomials in $\ell_2$ norm over the uniform distribution. This question is equivalent to understanding of the concentration of Fourier weight on low-degree coefficients, a central concept in Fourier analysis. We show that #R##N#1. For any submodular function $f:\{0,1\}^n \rightarrow [0,1]$, there is a polynomial of degree $O(\log (1/\epsilon) / \epsilon^{4/5})$ approximating $f$ within $\epsilon$ in $\ell_2$, and there is a submodular function that requires degree $\Omega(1/\epsilon^{4/5})$. #R##N#2. For any XOS function $f:\{0,1\}^n \rightarrow [0,1]$, there is a polynomial of degree $O(1/\epsilon)$ and there exists an XOS function that requires degree $\Omega(1/\epsilon)$. #R##N#This improves on previous approaches that all showed an upper bound of $O(1/\epsilon^2)$ for submodular and XOS functions. The best previous lower bound was $\Omega(1/\epsilon^{2/3})$ for monotone submodular functions. Our techniques reveal new structural properties of submodular and XOS functions and the upper bounds lead to nearly optimal PAC learning algorithms for these classes of functions.

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 "submodular optimization under noise"):
  with members:
    - "when are welfare guarantees robust"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "budget feasible mechanisms for experimental design"
    - "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"
    - "the computational complexity of truthfulness in combinatorial auctions"
    - "symmetry and approximability of submodular maximization problems"

Connected through hyperedge HE-3 (induced by source paper "learning combinatorial functions from pairwise comparisons"):
  with members:
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "learning coverage functions and private release of marginals"
    - "learning pseudo boolean k dnf and submodular functions"
    - "active ranking using pairwise comparisons"
    - "learning valuation functions"
    - "privately releasing conjunctions and the statistical query barrier"

Connected through hyperedge HE-4 (induced by source paper "the limitations of optimization from samples"):
  with members:
    - "introduction to online convex optimization"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "submodular optimization under noise"
    - "robust influence maximization"
    - "the sample complexity of revenue maximization"
    - "the pseudo dimension of near optimal auctions"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "mixture selection mechanism design and signaling"

Connected through hyperedge HE-5 (induced by source paper "tight bounds on ell_1 approximation and learning of self bounding functions"):
  with members:
    - "analysis of boolean functions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "learning valuation functions"
    - "submodular functions learnability structure and optimization"

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)