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 "capacitated dynamic programming faster knapsack and graph algorithms"):
  Abstract: One of the most fundamental problems in Theoretical Computer Science is the Knapsack problem. Given a set of n items with different weights and values, it asks to pick the most valuable subset whose total weight is below a capacity threshold T. Despite its wide applicability in various areas in computer science, operations research, and finance, the best known running time for the problem is O(T n). The main result of our work is an improvement of this algorithm for the special case where the number of distinct weights D is small, running in time O(T D). Previously, better algorithms for knapsack were known only for cases with weights and values bounded by M and V respectively, running in time O(nMV). In comparison, our algorithm implies a bound of O(nM^2) without any dependence on V. Additionally, for the unbounded knapsack problem, we provide an algorithm running in time O(M^2 log T). We also initiate a systematic study of general capacitated dynamic programming, of which knapsack is core problem. This problem asks to compute the maximum weight path of length k in an edge- or node-weighted directed acyclic graph. In a graph with m edges, these problems are solvable by dynamic programming in time O(km), and we explore under which conditions the dependence on k can be eliminated. We identify large classes of graphs where this is possible and apply our results to obtain linear time algorithms for the problem of k-sparse Delta-separated sequences. The main technical innovation behind our results is identifying and exploiting concavity that appears in relaxations and subproblems of the tasks we consider.

Includes members:
  Member "on problems equivalent to min convolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the fine grained complexity of parity problems")
  Member "fast algorithms for knapsack via convolution and prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating subset sum is equivalent to min plus convolution")
  Member "on integer programming and convolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "n fold integer programming via lp rounding")
  Member "fast algorithms for delta separated sparsity projection":
    also connected through other hyperedges:
      (none)
  Member "proximity results and faster algorithms for integer programming using the steinitz lemma":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "n fold integer programming via lp rounding")
  Member "a fast algorithm for separated sparsity via perturbed lagrangians":
    also connected through other hyperedges:
      (none)
  Member "a near linear pseudopolynomial time algorithm for subset sum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scheduling lower bounds via and subset sum")
  Member "faster all pairs shortest paths via circuit complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler")

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)