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: "improving the betweenness centrality of a node by adding links"
  Abstract: Betweenness is a well-known centrality measure that ranks the nodes according to their participation in the shortest paths of a network. In several scenarios, having a high betweenness can have a positive impact on the node itself. Hence, in this paper we consider the problem of determining how much a vertex can increase its centrality by creating a limited amount of new edges incident to it. In particular, we study the problem of maximizing the betweenness score of a given node -- Maximum Betweenness Improvement (MBI) -- and that of maximizing the ranking of a given node -- Maximum Ranking Improvement (MRI). We show that MBI cannot be approximated in polynomial-time within a factor $(1-\frac{1}{2e})$ and that MRI does not admit any polynomial-time constant factor approximation algorithm, both unless $P=NP$. We then propose a simple greedy approximation algorithm for MBI with an almost tight approximation ratio and we test its performance on several real-world networks. We experimentally show that our algorithm highly increases both the betweenness score and the ranking of a given node ant that it outperforms several competitive baselines. To speed up the computation of our greedy algorithm, we also propose a new dynamic algorithm for updating the betweenness of one node after an edge insertion, which might be of independent interest. Using the dynamic algorithm, we are now able to compute an approximation of MBI on networks with up to $10^5$ edges in most cases in a matter of seconds or a few minutes.

Connected through hyperedge HE-1 (induced by source paper "how to profit from payments channels"):
  with members:
    - "discharged payment channels quantifying the lightning network s resilience to topology based attacks"
    - "celer network bring internet scale to every blockchain"
    - "payment network design with fees"
    - "towards an economic analysis of routing in payment channel networks"
    - "sprites and state channels payment networks that go faster than lightning"
    - "how to charge lightning"
    - "settling payments fast and private efficient decentralized routing for path based transactions"

Connected through hyperedge HE-2 (induced by source paper "hijacking routes in payment channel networks a predictability tradeoff"):
  with members:
    - "discharged payment channels quantifying the lightning network s resilience to topology based attacks"
    - "decentralization in bitcoin and ethereum networks"
    - "a survey on the security of blockchain systems"
    - "hijacking bitcoin routing attacks on cryptocurrencies"
    - "hijacking bitcoin large scale network attacks on cryptocurrencies"
    - "analysis of hashrate based double spending"

Connected through hyperedge HE-3 (induced by source paper "on the fixed parameter tractability of the maximum connectivity improvement problem"):
  with members:
    - "coverage centrality maximization in undirected networks"
    - "path contractions edge deletions and connectivity preservation"
    - "the parameterized complexity of centrality improvement in networks"
    - "approximating minimum cost k node connected subgraphs via independence free graphs"

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)