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: "near optimal parallel algorithms for dynamic dfs in undirected graphs"
  Abstract: Depth first search (DFS) tree is a fundamental data structure for solving various graph problems. The classical algorithm [SIAMCOMP74] for building a DFS tree requires O(m+n) time for a given undirected graph G having n vertices and m edges. Recently, Baswana et al. [SODA16] presented a simple algorithm for updating the DFS tree of an undirected graph after an edge/vertex update in O (n) time. However, their algorithm is strictly sequential. We present an algorithm achieving similar bounds, that can be adopted easily to the parallel environment. In the parallel environment, a DFS tree can be computed from scratch using O(m) processors in expected O (1) time [SICOMP90] on an EREW PRAM, whereas the best deterministic algorithm takes O (√n) time [SIAMCOMP90,JAL93] on a CRCW PRAM. Our algorithm can be used to develop optimal (upto polylog n factors) deterministic algorithms for maintaining fully dynamic DFS and fault tolerant DFS, of an undirected graph.   1- Parallel Fully Dynamic DFS - Given any arbitrary online sequence of vertex or edge updates, we can maintain a DFS tree of an undirected graph in O (1) time per update using m processors on an EREW PRAM.   2- Parallel Fault tolerant DFS - An undirected graph can be preprocessed to build a data structure of size O(m) such that for a set of k updates (where k is constant) in the graph, a DFS tree of the updated graph can be computed in O (1) time using n processors on an EREW PRAM. For constant k, this is also work optimal (upto polylog n factors)   Moreover, our fully dynamic DFS algorithm provides, in a seamless manner, nearly optimal (upto polylog n factors) algorithms for maintaining a DFS tree in the semi-streaming environment and a restricted distributed model. These are the first parallel, semi-streaming and distributed algorithms for maintaining a DFS tree in the dynamic setting.

Connected through hyperedge HE-1 (induced by source paper "parallel batch dynamic graph connectivity"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "parallel working set search structures"
    - "improved worst case deterministic parallel dynamic minimum spanning forest"
    - "pam parallel augmented maps"
    - "batch parallel euler tour trees"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "fully dynamic minimum spanning forest with improved worst case update time"

Connected through hyperedge HE-2 (induced by source paper "depth first search in the semi streaming model"):
  with members:
    - "incremental dfs algorithms a theoretical and experimental study"
    - "distributed exact shortest paths in sublinear time"
    - "dynamic dfs in undirected graphs breaking the o m barrier"
    - "real time monitoring of undirected networks articulation points bridges and connected and biconnected components"
    - "better bounds for matchings in the streaming model"
    - "defining and evaluating network communities based on ground truth"
    - "superlinear lower bounds for multipass graph processing"
    - "linear programming in the semi streaming model with application to the maximum matching problem"

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)