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 approximate undirected shortest paths via low hop emulators"
  Abstract: We present a $(1+\varepsilon)$-approximate parallel algorithm for computing shortest paths in undirected graphs, achieving $\mathrm{poly}(\log n)$ depth and $m\mathrm{poly}(\log n)$ work for $n$-nodes $m$-edges graphs. Although sequential algorithms with (nearly) optimal running time have been known for several decades, near-optimal parallel algorithms have turned out to be a much tougher challenge. For $(1+\varepsilon)$-approximation, all prior algorithms with $\mathrm{poly}(\log n)$ depth perform at least $\Omega(mn^{c})$ work for some constant $c>0$. Improving this long-standing upper bound obtained by Cohen (STOC'94) has been open for $25$ years. #R##N#We develop several new tools of independent interest. One of them is a new notion beyond hopsets --- low hop emulator --- a $\mathrm{poly}(\log n)$-approximate emulator graph in which every shortest path has at most $O(\log\log n)$ hops (edges). Direct applications of the low hop emulators are parallel algorithms for $\mathrm{poly}(\log n)$-approximate single source shortest path (SSSP), Bourgain's embedding, metric tree embedding, and low diameter decomposition, all with $\mathrm{poly}(\log n)$ depth and $m\mathrm{poly}(\log n)$ work. #R##N#To boost the approximation ratio to $(1+\varepsilon)$, we introduce compressible preconditioners and apply it inside Sherman's framework (SODA'17) to solve the more general problem of uncapacitated minimum cost flow (a.k.a., transshipment problem). Our algorithm computes a $(1+\varepsilon)$-approximate uncapacitated minimum cost flow in $\mathrm{poly}(\log n)$ depth using $m\mathrm{poly}(\log n)$ work. As a consequence, it also improves the state-of-the-art sequential running time from $m\cdot 2^{O(\sqrt{\log n})}$ to $m\mathrm{poly}(\log n)$.

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"
    - "faster parallel algorithm for approximate shortest path"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "congested clique algorithms for graph spanners"

Connected through hyperedge HE-2 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space"):
  with members:
    - "walking randomly massively and efficiently"
    - "deterministic distributed dominating set approximation in the congest model"
    - "efficient deterministic distributed coloring with small bandwidth"
    - "exponentially faster massively parallel maximal matching"
    - "congested clique algorithms for graph spanners"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "faster parallel algorithm for approximate shortest path"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "nearly work efficient parallel algorithm for digraph reachability"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "approximate undirected maximum flows in o m polylog n time"
    - "a combinatorial tilde o m 3 2 time algorithm for the min cost flow problem"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "an efficient parallel solver for sdd linear systems"

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)