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: "fast distributed algorithms for connectivity and mst in large graphs"
  Abstract: Motivated by the increasing need to understand the algorithmic foundations of distributed large-scale graph computations, we study a number of fundamental graph problems in a message-passing model for distributed computing where $k \geq 2$ machines jointly perform computations on graphs with $n$ nodes (typically, $n \gg k$). The input graph is assumed to be initially randomly partitioned among the $k$ machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication rounds of the computation. #R##N#Our main result is an (almost) optimal distributed randomized algorithm for graph connectivity. Our algorithm runs in $\tilde{O}(n/k^2)$ rounds ($\tilde{O}$ notation hides a $\poly\log(n)$ factor and an additive $\poly\log(n)$ term). This improves over the best previously known bound of $\tilde{O}(n/k)$ [Klauck et al., SODA 2015], and is optimal (up to a polylogarithmic factor) in view of an existing lower bound of $\tilde{\Omega}(n/k^2)$. Our improved algorithm uses a bunch of techniques, including linear graph sketching, that prove useful in the design of efficient distributed graph algorithms. Using the connectivity algorithm as a building block, we then present fast randomized algorithms for computing minimum spanning trees, (approximate) min-cuts, and for many graph verification problems. All these algorithms take $\tilde{O}(n/k^2)$ rounds, and are optimal up to polylogarithmic factors. We also show an almost matching lower bound of $\tilde{\Omega}(n/k^2)$ rounds for many graph verification problems by leveraging lower bounds in random-partition communication complexity.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"

Connected through hyperedge HE-2 (induced by source paper "how fast can you update your mst dynamic algorithms for cluster computing"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "mst in o 1 rounds of the congested clique"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "optimal deterministic routing and sorting on the congested clique"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-3 (induced by source paper "efficient distributed community detection in the stochastic block model"):
  with members:
    - "large scale distributed algorithms for facility location with outliers"
    - "local mixing time distributed computation and applications"
    - "near optimal clustering in the k machine model"
    - "average whenever you meet opportunistic protocols for community detection"
    - "on the distributed complexity of large scale graph computations"
    - "find your place simple distributed algorithms for community detection"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "partitioning well clustered graphs spectral clustering works"

Connected through hyperedge HE-4 (induced by source paper "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory"):
  with members:
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "brief announcement semi mapreduce meets congested clique"
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "simple round compression for parallel vertex cover"

Connected through hyperedge HE-5 (induced by source paper "distributed computation in node capacitated networks"):
  with members:
    - "distributed symmetry breaking algorithms for congested cliques"
    - "competitive routing in hybrid communication networks"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "derandomizing local distributed algorithms under bandwidth restrictions"
    - "algebraic methods in the congested clique"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "distributed approximation algorithms for weighted shortest paths"

Connected through hyperedge HE-6 (induced by source paper "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"):
  with members:
    - "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory"
    - "simple graph coloring algorithms for congested clique and massively parallel computation"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "brief announcement semi mapreduce meets congested clique"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "an improved distributed algorithm for maximal independent set"

Connected through hyperedge HE-7 (induced by source paper "large scale distributed algorithms for facility location with outliers"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "on the distributed complexity of large scale graph computations"
    - "approximation of distances and shortest paths in the broadcast congest clique"
    - "distributed approximation algorithms for weighted shortest paths"
    - "lessons from the congested clique applied to mapreduce"
    - "distributed computation of large scale graph problems"
    - "optimal deterministic routing and sorting on the congested clique"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-8 (induced by source paper "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"):
  with members:
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "on estimating maximum matching size in graph streams"
    - "densest subgraph in dynamic graph streams"
    - "dynamic graph connectivity with improved worst case update time and sublinear space"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "an improved randomized data structure for dynamic graph connectivity"
    - "maximum matching in turnstile streams"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"

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.DC(Distributed, Parallel, and Cluster Computing)