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 computation of large scale graph problems"
  Abstract: Motivated by the increasing need for fast distributed processing of large-scale graphs such as the Web graph and various social networks, we study a message-passing distributed computing model for graph processing and present lower bounds and algorithms for several graph problems. This work is inspired by recent large-scale graph processing systems (e.g., Pregel and Giraph) which are designed based on the message-passing model of distributed computing. #R##N#Our model consists of a point-to-point communication network of $k$ machines interconnected by bandwidth-restricted links. Communicating data between the machines is the costly operation (as opposed to local computation). The network is used to process an arbitrary $n$-node input graph (typically $n \gg k > 1$) that is randomly partitioned among the $k$ machines (a common implementation in many real world systems). Our goal is to study fundamental complexity bounds for solving graph problems in this model. #R##N#We present techniques for obtaining lower bounds on the distributed time complexity. Our lower bounds develop and use new bounds in random-partition communication complexity. We first show a lower bound of $\Omega(n/k)$ rounds for computing a spanning tree (ST) of the input graph. This result also implies the same bound for other fundamental problems such as computing a minimum spanning tree (MST). We also show an $\Omega(n/k^2)$ lower bound for connectivity, ST verification and other related problems. #R##N#We give algorithms for various fundamental graph problems in our model. We show that problems such as PageRank, MST, connectivity, and graph covering can be solved in $\tilde{O}(n/k)$ time, whereas for shortest paths, we present algorithms that run in $\tilde{O}(n/\sqrt{k})$ time (for $(1+\epsilon)$-factor approx.) and in $\tilde{O}(n/k)$ time (for $O(\log n)$-factor approx.) respectively.

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 "exponentially faster shortest paths in the congested clique"):
  with members:
    - "near additive spanners and near exact hopsets a unified view"
    - "near additive spanners in low polynomial deterministic congest time"
    - "fast approximate shortest paths in the congested clique"
    - "congested clique algorithms for graph spanners"
    - "sparse matrix multiplication and triangle listing in the congested clique model"
    - "linear size hopsets with small hopbound and distributed routing with low memory"
    - "thorup zwick emulators are universally optimal hopsets"
    - "towards a complexity theory for the congested clique"

Connected through hyperedge HE-3 (induced by source paper "parallel approximate undirected shortest paths via low hop emulators"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "fast approximate shortest paths in the congested clique"
    - "brief announcement semi mapreduce meets congested clique"
    - "thorup zwick emulators are universally optimal hopsets"
    - "parallel shortest paths using radius stepping"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"

Connected through hyperedge HE-4 (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"
    - "fast distributed algorithms for connectivity and mst in large graphs"

Connected through hyperedge HE-5 (induced by source paper "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"):
  with members:
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"
    - "local flow partitioning for faster edge connectivity"
    - "on fully dynamic graph sparsifiers"
    - "powers of tensors and fast matrix multiplication"
    - "deterministic edge connectivity in near linear time"
    - "fast local computation algorithms"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-6 (induced by source paper "graph spanners in the message passing model"):
  with members:
    - "the 4 3 additive spanner exponent is tight"
    - "when distributed computation is communication expensive"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "tight bounds for distributed functional monitoring"

Connected through hyperedge HE-7 (induced by source paper "sparse matrix multiplication with bandwidth restricted all to all communication"):
  with members:
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "mst in o 1 rounds of the congested clique"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "towards a complexity theory for the congested clique"
    - "tight bounds for distributed graph computations"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "hypergraph partitioning for sparse matrix matrix multiplication"

Connected through hyperedge HE-8 (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"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"

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)