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 maximization problem with vector packing constraint"):
  Abstract: We consider the online vector packing problem in which we have a $d$ dimensional knapsack and items $u$ with weight vectors $\mathbf{w}_u \in \mathbb{R}_+^d$ arrive online in an arbitrary order. Upon the arrival of an item, the algorithm must decide immediately whether to discard or accept the item into the knapsack. When item $u$ is accepted, $\mathbf{w}_u(i)$ units of capacity on dimension $i$ will be taken up, for each $i\in[d]$. To satisfy the knapsack constraint, an accepted item can be later disposed of with no cost, but discarded or disposed of items cannot be recovered. The objective is to maximize the utility of the accepted items $S$ at the end of the algorithm, which is given by $f(S)$ for some non-negative monotone submodular function $f$. #R##N#For any small constant $\epsilon > 0$, we consider the special case that the weight of an item on every dimension is at most a $(1-\epsilon)$ fraction of the total capacity, and give a polynomial-time deterministic $O(\frac{k}{\epsilon^2})$-competitive algorithm for the problem, where $k$ is the (column) sparsity of the weight vectors. We also show several (almost) tight hardness results even when the algorithm is computationally unbounded. We show that under the $\epsilon$-slack assumption, no deterministic algorithm can obtain any $o(k)$ competitive ratio, and no randomized algorithm can obtain any $o(\frac{k}{\log k})$ competitive ratio. For the general case (when $\epsilon = 0$), no randomized algorithm can obtain any $o(k)$ competitive ratio. #R##N#In contrast to the $(1+\delta)$ competitive ratio achieved in Kesselheim et al. (STOC 2014) for the problem with random arrival order of items and under large capacity assumption, we show that in the arbitrary arrival order case, even when $\| \mathbf{w}_u \|_\infty$ is arbitrarily small for all items $u$, it is impossible to achieve any $o(\frac{\log k}{\log\log k})$ competitive ratio.

Includes members:
  Member "online submodular maximization with preemption":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale")
  Member "online bipartite matching with decomposable weights":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online truthful mechanisms for multi sided markets")
  Member "improved bounds for online preemptive matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prophet inequality for bipartite matching merits of being simple and non adaptive")
  Member "geometry of online packing linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online multidimensional packing problems in the random order model")
  Member "submodular function maximization via the multilinear relaxation and contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "online stochastic packing applied to display ad allocation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems")

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.DM(Discrete Mathematics)