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: "maximizing a monotone submodular function with a bounded curvature under a knapsack constraint"
  Abstract: We consider the problem of maximizing a monotone submodular function under a knapsack constraint. We show that, for any fixed $\epsilon > 0$, there exists a polynomial-time algorithm with an approximation ratio $1-c/e-\epsilon$, where $c \in [0,1]$ is the (total) curvature of the input function. This approximation ratio is tight up to $\epsilon$ for any $c \in [0,1]$. To the best of our knowledge, this is the first result for a knapsack constraint that incorporates the curvature to obtain an approximation ratio better than $1-1/e$, which is tight for general submodular functions. As an application of our result, we present a polynomial-time algorithm for the budget allocation problem with an improved approximation ratio.

Connected through hyperedge HE-1 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "towards nearly linear time algorithms for submodular maximization with a matroid constraint"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"

Connected through hyperedge HE-2 (induced by source paper "multi pass streaming algorithms for monotone submodular function maximization"):
  with members:
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "almost optimal streaming algorithms for coverage problems"
    - "streaming algorithms for submodular function maximization"
    - "lazier than lazy greedy"
    - "submodular maximization meets streaming matchings matroids and more"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-3 (induced by source paper "guarantees for greedy maximization of non submodular functions with applications"):
  with members:
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "greedy column subset selection new bounds and distributed algorithms"
    - "weakly submodular functions"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-4 (induced by source paper "a new approximation guarantee for monotone submodular function maximization via discrete convexity"):
  with members:
    - "stability and recovery for independence systems"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "learning mixtures of submodular shells with application to document summarization"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "combinatorial auctions with decreasing marginal utilities"

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)