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 convex covering and packing problems"):
  Abstract: We study the online convex covering problem and online convex packing problem. The (offline) convex covering problem is modeled by the following convex program: $\min_{x \in R_+^n} f(x) \ \text{s.t}\ A x \ge 1$, where $f : R_+^n \mapsto R_+$ is a monotone and convex cost function, and $A$ is an $m \times n$ matrix with non-negative entries. Each row of the constraint matrix $A$ corresponds to a covering constraint. In the online problem, each row of $A$ comes online and the algorithm must maintain a feasible assignment $x$ and may only increase $x$ over time. The (offline) convex packing problem is modeled by the following convex program: $\max_{y\in R_+^m} \sum_{j = 1}^m y_j - g(A^T y)$, where $g : R_+^n \mapsto R_+$ is a monotone and convex cost function. It is the Fenchel dual program of convex covering when $g$ is the convex conjugate of $f$. In the online problem, each variable $y_j$ arrives online and the algorithm must decide the value of $y_j$ on its arrival. #R##N#We propose simple online algorithms for both problems using the online primal dual technique, and obtain nearly optimal competitive ratios for both problems for the important special case of polynomial cost functions. For any convex polynomial cost functions with non-negative coefficients and maximum degree $\tau$, we introduce an $O(\tau \log n)^\tau$-competitive online convex covering algorithm, and an $O(\tau)$-competitive online convex packing algorithm, matching the known $\Omega(\tau \log n)^\tau$ and $\Omega(\tau)$ lower bounds respectively. #R##N#There is a large family of online resource allocation problems that can be modeled under this online convex covering and packing framework, including online covering and packing problems (with linear objectives), online mixed covering and packing, and online combinatorial auction. Our framework allows us to study these problems using a unified approach.

Includes members:
  Member "welfare maximization with production costs a primal dual approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reducing inefficiency in carbon auctions with imperfect competition")
  Member "online covering with convex objectives and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic service placement for mobile micro clouds with predicted future costs")
  Member "online packing and covering framework with convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online covering with sum of ell_q norm objectives")
  Member "approximating sparse covering integer programs online":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on approximating sparse covering integer programs")
  Member "online primal dual for non linear optimization with applications to speed scaling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "station assignment with reallocation")
  Member "welfare and profit maximization with production costs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reducing inefficiency in carbon auctions with imperfect competition")

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)