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: "an lmp o log n approximation algorithm for node weighted prize collecting steiner tree"
  Abstract: In the node-weighted prize-collecting Steiner tree problem (NW-PCST) we are given an undirected graph $G=(V,E)$, non-negative costs $c(v)$ and penalties $\pi(v)$ for each $v \in V$. The goal is to find a tree $T$ that minimizes the total cost of the vertices spanned by $T$ plus the total penalty of vertices not in $T$. This problem is well-known to be set-cover hard to approximate. Moss and Rabani (STOC'01) presented a primal-dual Lagrangean-multiplier-preserving $O(\ln |V|)$-approximation algorithm for this problem. We show a serious problem with the algorithm, and present a new, fundamentally different primal-dual method achieving the same performance guarantee. Our algorithm introduces several novel features to the primal-dual method that may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "approximating node weighted k mst on planar graphs"):
  with members:
    - "node weighted network design in planar and minor closed families of graphs"
    - "approximation algorithms for node weighted prize collecting steiner tree problems on planar graphs"
    - "improved approximation algorithms for budgeted node weighted steiner problems"
    - "lagrangian relaxation and partial cover"

Connected through hyperedge HE-2 (induced by source paper "cluster before you hallucinate approximating node capacitated network design and energy efficient routing"):
  with members:
    - "online primal dual for non linear optimization with applications to speed scaling"
    - "approximability of capacitated network design"
    - "spectral sparsification of graphs"
    - "an o k 3 log n approximation algorithm for vertex connectivity survivable network design"

Connected through hyperedge HE-3 (induced by source paper "covering problems in edge and node weighted graphs"):
  with members:
    - "improved approximation algorithms for budgeted node weighted steiner problems"
    - "spider covers for prize collecting network activation problem"

Connected through hyperedge HE-4 (induced by source paper "spider covers for prize collecting network activation problem"):
  with members:
    - "covering problems in edge and node weighted graphs"
    - "improved approximation algorithms for budgeted node weighted steiner problems"
    - "approximating subset k connectivity problems"
    - "an o k 3 log n approximation algorithm for vertex connectivity survivable network design"

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)