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 batch dynamic graph connectivity"
  Abstract: With the rapid growth of graph datasets over the past decade, a new kind of dynamic algorithm, supporting the ability to ingest batches of updates and exploit parallelism is needed in order to efficiently process large streams of updates. In this paper, we study batch and parallel algorithms for the dynamic connectivity problem, a fundamental problem that has received considerable attention in sequential setting. Perhaps the best known sequential algorithm is the elegant level-set algorithm of Holm, de Lichtenberg and Thorup (HDT), which achieves $O(\log^2 n)$ amortized time per edge insertion or deletion, and $O(\log n)$ time per query. #R##N#In this paper, we design a parallel batch-dynamic connectivity algorithm that is work-efficient with respect to the HDT algorithm for small batch sizes, and is asymptotically faster when the average batch size is sufficiently large. Given a sequence of batched updates, where $\Delta$ is the average batch size of all deletions, our algorithm achieves $O(\log n \log(1 + n / \Delta))$ expected amortized work per edge insertion and deletion and $O(\log^3 n)$ depth w.h.p. Our algorithm answers a batch of $k$ connectivity queries in $O(k \log(1 + n/k))$ expected work and $O(\log n)$ depth w.h.p. To the best of our knowledge, our algorithm is the first parallel batch-dynamic algorithm for connectivity.

Connected through hyperedge HE-1 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model"):
  with members:
    - "how fast can you update your mst dynamic algorithms for cluster computing"
    - "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"
    - "dynamic algorithms for the massively parallel computation model"
    - "fully dynamic maximal independent set in expected poly log update time"
    - "a deterministic algorithm for the mst problem in constant rounds of congested clique"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "fully dynamic maximal independent set with polylogarithmic update time"

Connected through hyperedge HE-2 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "batch parallel euler tour trees"
    - "round compression for parallel matching algorithms"

Connected through hyperedge HE-3 (induced by source paper "how fast can you update your mst dynamic algorithms for cluster computing"):
  with members:
    - "dynamic algorithms for the massively parallel computation model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "mst in o 1 rounds of the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "optimal deterministic routing and sorting on the congested clique"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-4 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model"):
  with members:
    - "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "improved worst case deterministic parallel dynamic minimum spanning forest"
    - "parallel minimum cuts in near linear work and low depth"
    - "faster fully dynamic minimum spanning forest"
    - "single pass sparsification in the streaming model with edge deletions"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-5 (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"
    - "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"
    - "livegraph a transactional graph storage system with purely sequential adjacency list scans"

Connected through hyperedge HE-6 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds"):
  with members:
    - "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"
    - "exponentially faster massively parallel maximal matching"
    - "distributed and streaming linear programming in low dimensions"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"

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)