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: "parallel metric tree embedding based on an algebraic view on moore bellman ford"
  Abstract: A \emph{metric tree embedding} of expected \emph{stretch~$\alpha \geq 1$} maps a weighted $n$-node graph $G = (V, E, \omega)$ to a weighted tree $T = (V_T, E_T, \omega_T)$ with $V \subseteq V_T$ such that, for all $v,w \in V$, $\operatorname{dist}(v, w, G) \leq \operatorname{dist}(v, w, T)$ and $operatorname{E}[\operatorname{dist}(v, w, T)] \leq \alpha \operatorname{dist}(v, w, G)$. Such embeddings are highly useful for designing fast approximation algorithms, as many hard problems are easy to solve on tree instances. However, to date the best parallel $(\operatorname{polylog} n)$-depth algorithm that achieves an asymptotically optimal expected stretch of $\alpha \in \operatorname{O}(\log n)$ requires $\operatorname{\Omega}(n^2)$ work and a metric as input. #R##N#In this paper, we show how to achieve the same guarantees using $\operatorname{polylog} n$ depth and $\operatorname{\tilde{O}}(m^{1+\epsilon})$ work, where $m = |E|$ and $\epsilon > 0$ is an arbitrarily small constant. Moreover, one may further reduce the work to $\operatorname{\tilde{O}}(m + n^{1+\epsilon})$ at the expense of increasing the expected stretch to $\operatorname{O}(\epsilon^{-1} \log n)$. #R##N#Our main tool in deriving these parallel algorithms is an algebraic characterization of a generalization of the classic Moore-Bellman-Ford algorithm. We consider this framework, which subsumes a variety of previous "Moore-Bellman-Ford-like" algorithms, to be of independent interest and discuss it in depth. In our tree embedding algorithm, we leverage it for providing efficient query access to an approximate metric that allows sampling the tree using $\operatorname{polylog} n$ depth and $\operatorname{\tilde{O}}(m)$ work. #R##N#We illustrate the generality and versatility of our techniques by various examples and a number of additional results.

Connected through hyperedge HE-1 (induced by source paper "massively parallel algorithms for distance approximation and spanners"):
  with members:
    - "walking randomly massively and efficiently"
    - "dynamic algorithms for the massively parallel computation model"
    - "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "faster parallel algorithm for approximate shortest path"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"

Connected through hyperedge HE-2 (induced by source paper "distributed construction of light networks"):
  with members:
    - "light spanners for high dimensional norms via stochastic decompositions"
    - "greedy spanners are optimal in doubling metrics"
    - "distributed exact shortest paths in sublinear time"
    - "improved distributed algorithms for exact shortest paths"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "constructing light spanners deterministically in near linear time"
    - "minor free graphs have light spanners"
    - "the greedy spanner is existentially optimal"

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 "low diameter graph decompositions by approximate distance computation"):
  with members:
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "a faster distributed single source shortest paths algorithm"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "on the complexity of local distributed graph problems"
    - "near optimal distributed maximum flow"
    - "simple parallel and distributed algorithms for spectral graph sparsification"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-5 (induced by source paper "new alpha beta spanners and hopsets"):
  with members:
    - "almost shortest paths and pram distance oracles in weighted graphs"
    - "linear size hopsets with small hopbound and distributed routing with low memory"
    - "thorup zwick emulators are universally optimal hopsets"
    - "distributed exact shortest paths in sublinear time"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "on efficient distributed construction of near optimal routing schemes"

Connected through hyperedge HE-6 (induced by source paper "almost shortest paths and pram distance oracles in weighted graphs"):
  with members:
    - "thorup zwick emulators are universally optimal hopsets"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "a linear size logarithmic stretch path reporting distance oracle for general graphs"
    - "the 4 3 additive spanner exponent is tight"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "decremental single source shortest paths on undirected graphs in near linear total update time"

Connected through hyperedge HE-7 (induced by source paper "lasserre integrality gaps for graph spanners and related problems"):
  with members:
    - "characterizing demand graphs for fixed parameter shallow light steiner network"
    - "approximating spanners and directed steiner forest upper and lower bounds"
    - "everywhere sparse spanners via dense subgraphs"
    - "label cover instances with large girth and the hardness of approximating basic k spanner"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "fault tolerant spanners better and simpler"
    - "directed steiner tree and the lasserre hierarchy"
    - "directed spanners via flow based linear programs"

Connected through hyperedge HE-8 (induced by source paper "massively parallel approximate distance sketches"):
  with members:
    - "fast approximate shortest paths in the congested clique"
    - "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "on efficient distributed construction of near optimal routing schemes"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "distributed computation of large scale graph problems"
    - "communication steps for parallel query processing"

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)