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: "incremental dfs algorithms a theoretical and experimental study"
  Abstract: Depth First Search (DFS) tree is a fundamental data structure for solving graph problems. The DFS tree of a graph $G$ with $n$ vertices and $m$ edges can be built in $O(m+n)$ time. Till date, only a few algorithms have been designed for maintaining incremental DFS. For undirected graphs, the two algorithms, namely, ADFS1 and ADFS2 [ICALP14] achieve total $O(n^{3/2}\sqrt{m})$ and $O(n^2)$ time respectively. For DAGs, the only non-trivial algorithm, namely, FDFS [IPL97] requires total $O(mn)$ time. #R##N#In this paper, we carry out extensive experimental and theoretical evaluation of existing incremental DFS algorithms in random and real graphs, and derive the following results. #R##N#1- For insertion of uniformly random sequence of $n \choose 2$ edges, ADFS1, ADFS2 and FDFS perform equally well and are found to take $\Theta(n^2)$ time experimentally. This is quite surprising because the worst case bounds of ADFS1 and FDFS are greater than $\Theta(n^2)$ by a factor of $\sqrt{m/n}$ and $m/n$ respectively. We complement this result by probabilistic analysis of these algorithms proving $\tilde{O}(n^2)$ bound on the update time. Here, we derive results about the structure of a DFS tree in random graphs, which are of independent interest. #R##N#2- These insights led us to design an extremely simple incremental DFS algorithm for both undirected and directed graphs. This algorithm theoretically matches and experimentally outperforms the state-of-the-art in dense random graphs. It can also be used as a single-pass semi-streaming algorithm for incremental DFS and strong connectivity in random graphs. #R##N#3- Even for real graphs, both ADFS1 and FDFS perform much better than their theoretical bounds. Here again, we present two simple algorithms for incremental DFS for directed and undirected real graphs. In fact, our algorithm for directed graphs almost always matches the performance of FDFS.

Connected through hyperedge HE-1 (induced by source paper "depth first search in the semi streaming model"):
  with members:
    - "distributed exact shortest paths in sublinear time"
    - "near optimal parallel algorithms for dynamic dfs in undirected graphs"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fault tolerant and fully dynamic dfs in undirected graphs simple yet efficient"):
  with members:
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"
    - "improved algorithms for maintaining dfs tree in undirected graphs"
    - "dynamic dfs in undirected graphs breaking the o m barrier"
    - "an improved algorithm for incremental dfs tree in undirected graphs"
    - "deterministic fully dynamic data structures for vertex cover and matching"
    - "dynamic connectivity connecting to networks and geometry"

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)