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 "improved algorithm for min cuts in distributed networks"):
  Abstract: In this thesis, we present fast deterministic algorithm to find small cuts in distributed networks. Finding small min-cuts for a network is essential for ensuring the quality of service and reliability. Throughout this thesis, we use the CONGEST model which is a typical message passing model used to design and analyze algorithms in distributed networks. We survey various algorithmic techniques in the CONGEST model and give an overview of the recent results to find cuts. We also describe elegant graph theoretic ideas like cut spaces and cycle spaces that provide useful intuition upon which our work is built. #R##N#Our contribution is a novel fast algorith to find small cuts. Our algorithm relies on a new characterization of trees and cuts introduced in this thesis. Our algorithm is built upon several new algorithmic ideas that, when coupled with our characterization of trees and cuts, help us to find the required min-cuts. Our novel techniques include a tree restricted semigroup function (TRSF), a novel sketching technique, and a layered algorithm. TRSF is defined with respect to a spanning tree and is based on a commutative semigroup. This simple yet powerful technique helps us to deterministically find min-cuts of size one (bridges) and min-cuts of size two optimally. Our sketching technique samples a small but relevant vertex set which is enough to find small min-cuts in certain cases. Our layered algorithm finds min-cuts in smaller sub-graphs pivoted by nodes at different levels in a spanning tree and uses them to make the decision about the min-cuts in the complete graph. This is interesting because it enables us to show that even for a global property like finding min-cuts, local information can be exploited in a coordinated manner.

Includes members:
  Member "a simple deterministic distributed mst algorithm with near optimal time and message complexities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed construction of light networks")
  Member "local flow partitioning for faster edge connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "a time and message optimal distributed algorithm for minimum spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed mst a smoothed analysis")
  Member "deterministic global minimum cut of a simple graph in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "almost tight distributed minimum cut algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "distributed minimum cut approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "distributed verification and hardness of distributed approximation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "fast computation of small cuts via cycle space sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed edge connectivity in sublinear time")

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)