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: "distributed minimum cut approximation"
  Abstract: We study the problem of computing approximate minimum edge cuts by distributed algorithms. We use a standard synchronous message passing model where in each round, $O(\log n)$ bits can be transmitted over each edge (a.k.a. the CONGEST model). We present a distributed algorithm that, for any weighted graph and any $\epsilon \in (0, 1)$, with high probability finds a cut of size at most $O(\epsilon^{-1}\lambda)$ in $O(D) + \tilde{O}(n^{1/2 + \epsilon})$ rounds, where $\lambda$ is the size of the minimum cut. This algorithm is based on a simple approach for analyzing random edge sampling, which we call the random layering technique. In addition, we also present another distributed algorithm, which is based on a centralized algorithm due to Matula [SODA '93], that with high probability computes a cut of size at most $(2+\epsilon)\lambda$ in $\tilde{O}((D+\sqrt{n})/\epsilon^5)$ rounds for any $\epsilon>0$. #R##N#The time complexities of both of these algorithms almost match the $\tilde{\Omega}(D + \sqrt{n})$ lower bound of Das Sarma et al. [STOC '11], thus leading to an answer to an open question raised by Elkin [SIGACT-News '04] and Das Sarma et al. [STOC '11]. #R##N#Furthermore, we also strengthen the lower bound of Das Sarma et al. by extending it to unweighted graphs. We show that the same lower bound also holds for unweighted multigraphs (or equivalently for weighted graphs in which $O(w\log n)$ bits can be transmitted in each round over an edge of weight $w$), even if the diameter is $D=O(\log n)$. For unweighted simple graphs, we show that even for networks of diameter $\tilde{O}(\frac{1}{\lambda}\cdot \sqrt{\frac{n}{\alpha\lambda}})$, finding an $\alpha$-approximate minimum cut in networks of edge connectivity $\lambda$ or computing an $\alpha$-approximation of the edge connectivity requires $\tilde{\Omega}(D + \sqrt{\frac{n}{\alpha\lambda}})$ rounds.

Connected through hyperedge HE-1 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest"):
  with members:
    - "improved distributed approximation to maximum independent set"
    - "detecting cliques in congest networks"
    - "distributed spanner approximation"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "distributed approximation of maximum independent set and maximum matching"
    - "near linear lower bounds for distributed distance computations even in sparse networks"
    - "distributed construction of purely additive spanners"

Connected through hyperedge HE-2 (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"
    - "almost tight distributed minimum cut algorithms"
    - "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-3 (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-4 (induced by source paper "what graph neural networks cannot learn depth vs width"):
  with members:
    - "edgenets edge varying graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mincut pooling in graph neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"

Connected through hyperedge HE-5 (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-6 (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"
    - "almost tight distributed minimum cut algorithms"
    - "fault tolerant spanners better and simpler"

Connected through hyperedge HE-7 (induced by source paper "a simple algorithm for minimum cuts in near linear time"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "practical minimum cut algorithms"
    - "parallel minimum cuts in near linear work and low depth"
    - "local flow partitioning for faster edge connectivity"
    - "incremental exact min cut in poly logarithmic amortized update time"
    - "deterministic edge connectivity in near linear time"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-8 (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"
    - "almost tight distributed minimum cut algorithms"

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)