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: "an almost tight distributed algorithm for computing single source shortest paths"
  Abstract: We present a deterministic $(1+o(1))$-approximation $O(n^{1/2+o(1)}+D^{1+o(1)})$-time algorithm for solving the single-source shortest paths problem on distributed weighted networks (the CONGEST model); here $n$ is the number of nodes in the network and $D$ is its (hop) diameter. This is the first non-trivial deterministic algorithm for this problem. It also improves (i) the running time of the randomized $(1+o(1))$-approximation $\tilde O(n^{1/2}D^{1/4}+D)$-time algorithm of Nanongkai [STOC 2014] by a factor of as large as $n^{1/8}$, and (ii) the $O(\epsilon^{-1}\log \epsilon^{-1})$-approximation factor of Lenzen and Patt-Shamir's $\tilde O(n^{1/2+\epsilon}+D)$-time algorithm [STOC 2013] within the same running time. Our running time matches the known time lower bound of $\Omega(n^{1/2}/\log n + D)$ [Das Sarma et al. STOC 2011] modulo some lower-order terms, thus essentially settling the status of this problem which was raised at least a decade ago [Elkin, SIGACT News 2004]. It also implies a $(2+o(1))$-approximation $O(n^{1/2+o(1)}+D^{1+o(1)})$-time algorithm for approximating a network's weighted diameter which almost matches the lower bound by Holzer et al. [PODC 2012]. In achieving this result, we develop two techniques which might be of independent interest and useful in other settings: (i) a deterministic process that replaces the "hitting set argument" commonly used for shortest paths computation in various settings, and (ii) a simple, deterministic, construction of an $(n^{o(1)}, o(1))$-hop set of size $O(n^{1+o(1)})$. We combine these techniques with many distributed algorithmic techniques, some of which from problems that are not directly related to shortest paths, e.g. ruling sets [Goldberg et al. STOC 1987], source detection [Lenzen, Peleg PODC 2013], and partial distance estimation [Lenzen, Patt-Shamir PODC 2015].

Connected through hyperedge HE-1 (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-2 (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"
    - "near optimal distributed maximum flow"
    - "fast partial distance estimation and applications"
    - "almost tight distributed minimum cut algorithms"
    - "distributed approximation algorithms for weighted shortest paths"

Connected through hyperedge HE-3 (induced by source paper "faster distributed shortest path approximations via shortcuts"):
  with members:
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "low congestion shortcuts without embedding"
    - "distributed strong diameter network decomposition"
    - "fast partial distance estimation and applications"
    - "almost tight distributed minimum cut algorithms"
    - "distributed approximation algorithms for weighted shortest paths"
    - "cops robbers and threatening skeletons padded decomposition for minor free graphs"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-4 (induced by source paper "estimation of graphlet statistics"):
  with members:
    - "role discovery in networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "graph based anomaly detection and description a survey"
    - "sensing as a service and big data"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-5 (induced by source paper "near linear lower bounds for distributed distance computations even in sparse networks"):
  with members:
    - "distributed construction of purely additive spanners"
    - "algebraic methods in the congested clique"
    - "subtree isomorphism revisited"
    - "approximation of distances and shortest paths in the broadcast congest clique"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "distributed approximation algorithms for weighted shortest paths"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-6 (induced by source paper "hopsets with constant hopbound and applications to approximate shortest paths"):
  with members:
    - "on efficient distributed construction of near optimal routing schemes"
    - "decremental single source shortest paths on undirected graphs in near linear total update time"
    - "fast partial distance estimation and applications"
    - "distributed approximation algorithms for weighted shortest paths"
    - "improved parallel algorithms for spanners and hopsets"

Connected through hyperedge HE-7 (induced by source paper "parallel metric tree embedding based on an algebraic view on moore bellman ford"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "efficient construction of probabilistic tree embeddings"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "fast partial distance estimation and applications"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-8 (induced by source paper "fast partial distance estimation and applications"):
  with members:
    - "approximation of distances and shortest paths in the broadcast congest clique"
    - "distributed approximation algorithms for weighted shortest paths"
    - "improved distributed steiner forest construction"
    - "efficient computation of distance sketches in distributed networks"
    - "distributed verification and hardness of distributed approximation"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

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)