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: "approximate undirected transshipment and shortest paths via gradient descent"
  Abstract: We present a method for solving the transshipment problem - also known as uncapacitated minimum cost flow - up to a multiplicative error of $1 + \epsilon$ in undirected graphs with polynomially bounded integer edge weights using a tailored gradient descent algorithm. An important special case of the transshipment problem is the single-source shortest paths (SSSP) problem. Our gradient descent algorithm takes $O(\epsilon^{-3} \mathrm{polylog} n)$ iterations and in each iteration it needs to solve a variant of the transshipment problem up to a multiplicative error of $\mathrm{polylog} n$. In particular, this allows us to perform a single iteration by computing a solution on a sparse spanner of logarithmic stretch. As a consequence, we improve prior work by obtaining the following results: (1) RAM model: $(1+\epsilon)$-approximate transshipment in $\tilde{O}(\epsilon^{-3}(m + n^{1 + o(1)}))$ computational steps (leveraging a recent $O(m^{1+o(1)})$-step $O(1)$-approximation due to Sherman [2016]). (2) Multipass Streaming model: $(1 + \epsilon)$-approximate transshipment and SSSP using $\tilde{O}(n) $ space and $\tilde{O}(\epsilon^{-O(1)})$ passes. (3) Broadcast Congested Clique model: $(1 + \epsilon)$-approximate transshipment and SSSP using $\tilde{O}(\epsilon^{-O(1)})$ rounds. (4) Broadcast Congest model: $(1 + \epsilon)$-approximate SSSP using $\tilde{O}(\epsilon^{-O(1)}(\sqrt{n} + D))$ rounds, where $ D $ is the (hop) diameter of the network. The previous fastest algorithms for the last three models above leverage sparse hop sets. We bypass the hop set computation; using a spanner is sufficient in our method. The above bounds assume non-negative integer edge weights that are polynomially bounded in $n$; for general non-negative weights, running times scale with the logarithm of the maximum ratio between non-zero weights.

Connected through hyperedge HE-1 (induced by source paper "distributed exact shortest paths in sublinear time"):
  with members:
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "algebraic methods in the congested clique"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "approximation of distances and shortest paths in the broadcast congest clique"
    - "almost tight distributed minimum cut algorithms"
    - "distributed approximation algorithms for weighted shortest paths"
    - "distributed minimum cut approximation"

Connected through hyperedge HE-2 (induced by source paper "improved distributed algorithms for exact shortest paths"):
  with members:
    - "distributed exact shortest paths in sublinear time"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "almost tight distributed minimum cut algorithms"
    - "distributed approximation algorithms for weighted shortest paths"
    - "distributed minimum cut approximation"
    - "distributed verification and hardness of distributed approximation"

Connected through hyperedge HE-3 (induced by source paper "near optimal clustering in the k machine model"):
  with members:
    - "tight bounds for distributed graph computations"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "scalable facility location for massive graphs on pregel like systems"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "distributed computation of large scale graph problems"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-4 (induced by source paper "how bandwidth affects the congest model"):
  with members:
    - "distributed approximation algorithms for weighted shortest paths"

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)