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: "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 $0.51$.

Connected through hyperedge HE-1 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization beyond 1 e"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"

Connected through hyperedge HE-2 (induced by source paper "beyond submodular maximization"):
  with members:
    - "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "local search for max sum diversification"
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "max sum diversity via convex programming"
    - "building a good team secretary problems and the supermodular degree"
    - "a new framework for distributed submodular maximization"

Connected through hyperedge HE-3 (induced by source paper "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"):
  with members:
    - "deterministic 1 2 epsilon approximation for submodular maximization over a matroid"
    - "online submodular maximization beating 1 2 made simple"
    - "do less get more streaming submodular maximization with subsampling"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "constrained submodular maximization beyond 1 e"
    - "streaming algorithms for submodular function maximization"
    - "online submodular maximization with preemption"
    - "deterministic algorithms for submodular maximization problems"

Connected through hyperedge HE-4 (induced by source paper "maximizing welfare in social networks under a utility driven influence diffusion model"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "inferring networks of substitutable and complementary products"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-5 (induced by source paper "online submodular maximization beating 1 2 made simple"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "deterministic 1 2 epsilon approximation for submodular maximization over a matroid"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "online matroid intersection beating half for random arrival"
    - "lazier than lazy greedy"
    - "online submodular welfare maximization greedy is optimal"
    - "maximum matching in semi streaming with few passes"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-6 (induced by source paper "submodular secretary problem with shortlists"):
  with members:
    - "online submodular maximization beating 1 2 made simple"
    - "the submodular secretary problem under a cardinality constraint and with limited resources"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "the submodular secretary problem goes linear"
    - "lazier than lazy greedy"
    - "submodular maximization meets streaming matchings matroids and more"
    - "online submodular welfare maximization greedy is optimal"
    - "online stochastic packing applied to display ad allocation"

Connected through hyperedge HE-7 (induced by source paper "beyond 1 2 approximation for submodular maximization on massive data streams"):
  with members:
    - "deletion robust submodular maximization at scale"
    - "better streaming algorithms for the maximum coverage problem"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "online submodular maximization with preemption"
    - "a new framework for distributed submodular maximization"
    - "lazier than lazy greedy"
    - "submodular maximization meets streaming matchings matroids and more"

Connected through hyperedge HE-8 (induced by source paper "submodular maximization under a matroid constraint asking more from an old friend the greedy algorithm"):
  with members:
    - "a new approximation guarantee for monotone submodular function maximization via discrete convexity"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

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)