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: "almost tight distributed minimum cut algorithms"
  Abstract: We study the problem of computing the minimum cut in a weighted distributed message-passing networks (the CONGEST model). Let $\lambda$ be the minimum cut, $n$ be the number of nodes in the network, and $D$ be the network diameter. Our algorithm can compute $\lambda$ exactly in $O((\sqrt{n} \log^{*} n+D)\lambda^4 \log^2 n)$ time. To the best of our knowledge, this is the first paper that explicitly studies computing the exact minimum cut in the distributed setting. Previously, non-trivial sublinear time algorithms for this problem are known only for unweighted graphs when $\lambda\leq 3$ due to Pritchard and Thurimella's $O(D)$-time and $O(D+n^{1/2}\log^* n)$-time algorithms for computing $2$-edge-connected and $3$-edge-connected components. #R##N#By using the edge sampling technique of Karger's, we can convert this algorithm into a $(1+\epsilon)$-approximation $O((\sqrt{n}\log^{*} n+D)\epsilon^{-5}\log^3 n)$-time algorithm for any $\epsilon>0$. This improves over the previous $(2+\epsilon)$-approximation $O((\sqrt{n}\log^{*} n+D)\epsilon^{-5}\log^2 n\log\log n)$-time algorithm and $O(\epsilon^{-1})$-approximation $O(D+n^{\frac{1}{2}+\epsilon} \mathrm{poly}\log n)$-time algorithm of Ghaffari and Kuhn. Due to the lower bound of $\Omega(D+n^{1/2}/\log n)$ by Das Sarma et al. which holds for any approximation algorithm, this running time is tight up to a $ \mathrm{poly}\log n$ factor. #R##N#To get the stated running time, we developed an approximation algorithm which combines the ideas of Thorup's algorithm and Matula's contraction algorithm. It saves an $\epsilon^{-9}\log^{7} n$ factor as compared to applying Thorup's tree packing theorem directly. Then, we combine Kutten and Peleg's tree partitioning algorithm and Karger's dynamic programming to achieve an efficient distributed algorithm that finds the minimum cut when we are given a spanning tree that crosses the minimum cut exactly once.

Connected through hyperedge HE-1 (induced by source paper "improved algorithm for min cuts in distributed networks"):
  with members:
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "local flow partitioning for faster edge connectivity"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "distributed minimum cut approximation"
    - "distributed verification and hardness of distributed approximation"
    - "fast computation of small cuts via cycle space sampling"
    - "fast distributed computation of cuts via random circulations"

Connected through hyperedge HE-2 (induced by source paper "distributed edge connectivity in sublinear time"):
  with members:
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "distributed triangle detection via expander decomposition"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "expander decomposition and pruning faster stronger and simpler"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"

Connected through hyperedge HE-3 (induced by source paper "hardness of distributed optimization"):
  with members:
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "fast and efficient distributed computation of hamiltonian cycles in random graphs"
    - "a distributed algorithm for finding hamiltonian cycles in random graphs in o log n time"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "parameterized distributed algorithms"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "detecting cliques in congest networks"
    - "simple and local independent set approximation"

Connected through hyperedge HE-4 (induced by source paper "faster algorithms for edge connectivity via random 2 out contractions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "distributed edge connectivity in sublinear time"
    - "random k out subgraph leaves only o n k inter component edges"
    - "exponentially faster massively parallel maximal matching"
    - "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-5 (induced by source paper "small cuts and connectivity certificates a fault tolerant approach"):
  with members:
    - "deterministic combinatorial replacement paths and distance sensitivity oracles"
    - "distributed edge connectivity in sublinear time"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "low congestion cycle covers and their applications"
    - "distributed approximation of minimum k edge connected spanning subgraphs"
    - "fast distributed approximation for tap and 2 edge connectivity"
    - "distributed minimum cut approximation"
    - "fault tolerant spanners better and simpler"

Connected through hyperedge HE-6 (induced by source paper "improved distributed approximations for minimum weight two edge connected spanning subgraph"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "minor excluded network families admit fast distributed algorithms"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "distributed approximation of minimum k edge connected spanning subgraphs"
    - "fast distributed approximation for tap and 2 edge connectivity"
    - "low congestion shortcuts without embedding"
    - "distributed minimum cut approximation"

Connected through hyperedge HE-7 (induced by source paper "distributed exact weighted all pairs shortest paths in near linear time"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "new and simplified distributed algorithms for weighted all pairs shortest paths"
    - "a deterministic distributed algorithm for weighted all pairs shortest paths through pipelining"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"
    - "distributed exact weighted all pairs shortest paths in tilde o n 5 4 rounds"
    - "improved distributed algorithms for exact shortest paths"

Connected through hyperedge HE-8 (induced by source paper "minor excluded network families admit fast distributed algorithms"):
  with members:
    - "distributed exact shortest paths in sublinear time"
    - "distributed exact weighted all pairs shortest paths in tilde o n 5 4 rounds"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "low congestion shortcuts without embedding"
    - "an almost tight distributed algorithm for computing single source shortest paths"
    - "near optimal distributed maximum flow"
    - "fast partial distance estimation and applications"
    - "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)