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: "dynamic approximate shortest paths and beyond subquadratic and worst case update time"
  Abstract: Consider the following distance query for an $n$-node graph $G$ undergoing edge insertions and deletions: given two sets of nodes $I$ and $J$, return the distances between every pair of nodes in $I\times J$. This query is rather general and captures several versions of the dynamic shortest paths problem. In this paper, we develop an efficient $(1+\epsilon)$-approximation algorithm for this query using fast matrix multiplication. Our algorithm leads to answers for some open problems for Single-Source and All-Pairs Shortest Paths (SSSP and APSP), as well as for Diameter, Radius, and Eccentricities. Below are some highlights. Note that all our algorithms guarantee worst-case update time and are randomized (Monte Carlo), but do not need the oblivious adversary assumption. #R##N#Subquadratic update time for SSSP, Diameter, Centralities, ect.: When we want to maintain distances from a single node explicitly (without queries), a fundamental question is to beat trivially calling Dijkstra's static algorithm after each update, taking $\Theta(n^2)$ update time on dense graphs. It was known to be improbable for exact algorithms and for combinatorial any-approximation algorithms to polynomially beat the $\Omega(n^2)$ bound (under some conjectures) [Roditty, Zwick, ESA'04; Abboud, V. Williams, FOCS'14]. Our algorithm with $I=\{s\}$ and $J=V(G)$ implies a $(1+\epsilon)$-approximation algorithm for this, guaranteeing $\tilde O(n^{1.823}/\epsilon^2)$ worst-case update time for directed graphs with positive real weights in $[1, W]$. With ideas from [Roditty, V. Williams, STOC'13], we also obtain the first subquadratic worst-case update time for $(5/3+\epsilon)$-approximating the eccentricities and $(1.5+\epsilon)$-approximating the diameter and radius for unweighted graphs (with small additive errors). #R##N#[...]

Connected through hyperedge HE-1 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler"):
  with members:
    - "decremental sssp in weighted digraphs faster and against an adaptive adversary"
    - "fully dynamic all pairs shortest paths improved worst case time and space bounds"
    - "decremental strongly connected components and single source reachability in near linear time"
    - "algorithms and hardness for diameter in dynamic graphs"
    - "diameter spanner eccentricity spanner and approximating extremal graph distances static dynamic and fault tolerant"
    - "deterministic partially dynamic single source shortest paths in weighted graphs"
    - "thorup zwick emulators are universally optimal hopsets"
    - "tight hardness for shortest cycles and paths in sparse graphs"

Connected through hyperedge HE-2 (induced by source paper "fully dynamic all pairs shortest paths improved worst case time and space bounds"):
  with members:
    - "deterministic algorithms for decremental approximate shortest paths faster and simpler"
    - "dynamic decremental approximate distance oracles with 1 e 2 stretch"
    - "dynamic approximate all pairs shortest paths breaking the o mn barrier and derandomization"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-4 (induced by source paper "dynamic matrix inverse improved algorithms and matching conditional lower bounds"):
  with members:
    - "a deterministic linear program solver in current matrix multiplication time"
    - "sensitive distance and reachability oracles for large batch updates"
    - "solving linear programs in the current matrix multiplication time"
    - "online bipartite matching with amortized o log 2 n replacements"
    - "tight cell probe bounds for succinct boolean matrix vector multiplication"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic minimum spanning forest with improved worst case update time"

Connected through hyperedge HE-5 (induced by source paper "sensitive distance and reachability oracles for large batch updates"):
  with members:
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "deterministic combinatorial replacement paths and distance sensitivity oracles"
    - "conditional hardness for sensitivity problems"
    - "fast deterministic computation of the hermite normal form and determinant of a polynomial matrix"
    - "connectivity oracles for graphs subject to vertex failures"
    - "incremental and fully dynamic subgraph connectivity for emergency planning"
    - "powers of tensors and fast matrix multiplication"
    - "popular conjectures imply strong lower bounds for dynamic problems"

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)