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: "faster and simpler sketches of valuation functions"
  Abstract: We present fast algorithms for sketching valuation functions. Let $N$ ($|N|=n$) be some ground set and $v:2^N\rightarrow \mathbb R$ be a function. We say that $\tilde v:2^N\rightarrow \mathbb R$ is an $\alpha$-sketch of $v$ if for every set $S$ we have that $\frac {v(S)} {\alpha} \leq \tilde v(S) \leq v(S)$ and $\tilde v$ can be described in $poly(n)$ bits. #R##N#Goemans et al. [SODA'09] showed that if $v$ is submodular then there exists an $\tilde O(\sqrt n)$-sketch that can be constructed using polynomially many value queries (this is the best possible, as Balcan and Harvey [STOC'11] show that no submodular function admit an $n^{\frac 1 3 - \epsilon}$-sketch). Based on their work, Balcan et al. [COLT'12] and Badanidiyuru et al. [SODA'12] show that if $v$ is subadditive then there exists an $\tilde O(\sqrt n)$-sketch that can be constructed using polynomially many demand queries. All previous sketches are based on complicated geometric constructions. The first step in their constructions is proving the existence of a good sketch by finding an ellipsoid that ``approximates'' $v$ well (this is done by applying John's theorem to ensure the existence of an ellipsoid that is ``close'' to the polymatroid that is associated with $v$). The second step is showing this ellipsoid can be found efficiently, and this is done by repeatedly solving a certain convex program to obtain better approximations of John's ellipsoid. #R##N#In this paper, we give a much simpler, non-geometric proof for the existence of good sketches, and utilize the proof to obtain much faster algorithms that match the previously obtained approximation bounds. Specifically, we provide an algorithm that finds $\tilde O(\sqrt n)$-sketch of a submodular function with only $\tilde O(n^\frac{3}{2})$ value queries, and an algorithm that finds $\tilde O(\sqrt n)$-sketch of a subadditive function with $O(n)$ demand and value queries.

Connected through hyperedge HE-1 (induced by source paper "sketching with test scores and submodular maximization"):
  with members:
    - "submodular optimization under noise"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "team performance with test scores"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-2 (induced by source paper "submodular maximization using test scores"):
  with members:
    - "submodular optimization under noise"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "team performance with test scores"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-3 (induced by source paper "optimization with demand oracles"):
  with members:
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "budget feasible mechanism design from prior free to bayesian"
    - "approximations for monotone and non monotone submodular maximization with knapsack constraints"
    - "budget feasible mechanism design via random sampling"
    - "an impossibility result for truthful combinatorial auctions with submodular valuations"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

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.GT(Computer Science and Game Theory)