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 "online submodular welfare maximization greedy beats 1 2 in random order"):
  Abstract: In the Submodular Welfare Maximization (SWM) problem, the input consists of a set of $n$ items, each of which must be allocated to one of $m$ agents. Each agent $\ell$ has a valuation function $v_\ell$, where $v_\ell(S)$ denotes the welfare obtained by this agent if she receives the set of items $S$. The functions $v_\ell$ are all submodular; as is standard, we assume that they are monotone and $v_\ell(\emptyset) = 0$. The goal is to partition the items into $m$ disjoint subsets $S_1, S_2, \ldots S_m$ in order to maximize the social welfare, defined as $\sum_{\ell = 1}^m v_\ell(S_\ell)$. #R##N#In this paper, we consider the online version of SWM. Here, items arrive one at a time in an online manner; when an item arrives, the algorithm must make an irrevocable decision about which agent to assign it to before seeing any subsequent items. This problem is motivated by applications to Internet advertising, where user ad impressions must be allocated to advertisers whose value is a submodular function of the set of users / impressions they receive. In the random order model, the adversary can construct a worst-case set of items and valuations, but does not control the order in which the items arrive; instead, they are assumed to arrive in a random order. Obtaining a competitive ratio of $1/2 + \Omega(1)$ for the random order model has been an important open problem for several years. We solve this open problem by demonstrating that the greedy algorithm has a competitive ratio of at least $0.505$ for the Online Submodular Welfare Maximization problem in the random order model. For special cases of submodular functions including weighted matching, weighted coverage functions and a broader class of "second-order supermodular" functions, we provide a different analysis that gives a competitive ratio of $0.51$.

Includes members:
  Member "near optimal online algorithms and fast approximation algorithms for resource allocation problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models")
  Member "randomized composable core sets for distributed submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized submodular maximization at scale")
  Member "bayesian sequential auctions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the price of anarchy in auctions")
  Member "online submodular welfare maximization greedy is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")
  Member "geometry of online packing linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online multidimensional packing problems in the random order model")
  Member "composable and efficient mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "sequential auctions and externalities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders")
  Member "online vertex weighted bipartite matching and single bid budgeted allocations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online graph matching problems with a worst case reassignment budget")

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)