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 "approximating subset sum is equivalent to min plus convolution"):
  Abstract: Approximating Subset Sum is a classic and fundamental problem in computer science and mathematical optimization. The state-of-the-art approximation scheme for Subset Sum computes a $(1-\varepsilon)$-approximation in time $\tilde{O}(\min\{n/\varepsilon, n+1/\varepsilon^2\})$ [Gens, Levner'78, Kellerer et al.'97]. In particular, a $(1-1/n)$-approximation can be computed in time $O(n^2)$. #R##N#We establish a connection to the Min-Plus-Convolution problem, which is of particular interest in fine-grained complexity theory and can be solved naively in time $O(n^2)$. Our main result is that computing a $(1-1/n)$-approximation for Subset Sum is subquadratically equivalent to Min-Plus-Convolution. Thus, assuming the Min-Plus-Convolution conjecture from fine-grained complexity theory, there are no approximation schemes for Subset Sum with strongly subquadratic dependence on $n$ and $1/\varepsilon$. In the other direction, our reduction allows us to transfer known lower order improvements from Min-Plus-Convolution to Subset Sum, which yields a mildly subquadratic approximation scheme. This adds the first approximation problem to the list of Min-Plus-Convolution-equivalent problems.

Includes members:
  Member "an improved fptas for 0 1 knapsack":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximation schemes for subset sum ratio problems")
  Member "approximating apsp without scaling equivalence of approximate min plus and exact min max":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shortest path centrality and the all pairs shortest paths problem via vc dimension theory")
  Member "on problems equivalent to min convolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the fine grained complexity of parity problems")
  Member "subquadratic approximation scheme for partition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating apsp without scaling equivalence of approximate min plus and exact min max")
  Member "fast algorithms for knapsack via convolution and prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "massively parallel algorithms for string matching with wildcards")
  Member "an equivalence class for orthogonal vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search")
  Member "on integer programming and convolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "n fold integer programming via lp rounding")
  Member "on the hardness of approximate and exact bichromatic maximum inner product":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating apsp without scaling equivalence of approximate min plus and exact min max")

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)