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: "subquadratic approximation scheme for partition"
  Abstract: The subject of this paper is the time complexity of approximating Knapsack, Subset Sum, Partition, and some other related problems. The main result is an $\widetilde{O}(n+1/\varepsilon^{5/3})$ time randomized FPTAS for Partition, which is derived from a certain relaxed form of a randomized FPTAS for Subset Sum. To the best of our knowledge, this is the first NP-hard problem that has been shown to admit a subquadratic time approximation scheme, i.e., one with time complexity of $O((n+1/\varepsilon)^{2-\delta})$ for some $\delta>0$. To put these developments in context, note that a quadratic FPTAS for Partition has been known for 40 years. #R##N#Our main contribution lies in designing a mechanism that reduces an instance of Subset Sum to several simpler instances, each with some special structure, and keeps track of interactions between them. This allows us to combine techniques from approximation algorithms, pseudo-polynomial algorithms, and additive combinatorics. #R##N#We also prove several related results. Notably, we improve approximation schemes for 3-SUM, (min,+)-convolution, and Tree Sparsity. Finally, we argue why breaking the quadratic barrier for approximate Knapsack is unlikely by giving an $\Omega((n+1/\varepsilon)^{2-o(1)})$ conditional lower bound.

Connected through hyperedge HE-1 (induced by source paper "approximating subset sum is equivalent to min plus convolution"):
  with members:
    - "an improved fptas for 0 1 knapsack"
    - "approximating apsp without scaling equivalence of approximate min plus and exact min max"
    - "on problems equivalent to min convolution"
    - "fast algorithms for knapsack via convolution and prediction"
    - "an equivalence class for orthogonal vectors"
    - "on integer programming and convolution"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "a simple near linear pseudopolynomial time randomized algorithm for subset sum"

Connected through hyperedge HE-2 (induced by source paper "approximating apsp without scaling equivalence of approximate min plus and exact min max"):
  with members:
    - "on problems equivalent to min convolution"
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "from gap eth to fpt inapproximability clique dominating set and more"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "distributed pcp theorems for hardness of approximation in p"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "a fast numerical method for max convolution and the application to efficient max product inference in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "an improved fptas for 0 1 knapsack"):
  with members:
    - "on problems equivalent to min convolution"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "a faster fptas for the unbounded knapsack problem"
    - "faster all pairs shortest paths via circuit complexity"
    - "necklaces convolutions and x y"

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)