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: "analyzing the optimal neighborhood algorithms for budgeted and partial connected dominating set problems"
  Abstract: We study partial and budgeted versions of the well studied connected dominating set problem. In the partial connected dominating set problem, we are given an undirected graph G = (V,E) and an integer n', and the goal is to find a minimum subset of vertices that induces a connected subgraph of G and dominates at least n' vertices. We obtain the first polynomial time algorithm with an O(\ln \Delta) approximation factor for this problem, thereby significantly extending the results of Guha and Khuller (Algorithmica, Vol. 20(4), Pages 374-387, 1998) for the connected dominating set problem. We note that none of the methods developed earlier can be applied directly to solve this problem. In the budgeted connected dominating set problem, there is a budget on the number of vertices we can select, and the goal is to dominate as many vertices as possible. We obtain a (1/13)(1 - 1/e) approximation algorithm for this problem. Finally, we show that our techniques extend to a more general setting where the profit function associated with a subset of vertices is a monotone "special" submodular function. This generalization captures the connected dominating set problem with capacities and/or weighted profits as special cases. This implies a O(\ln q) approximation (where q denotes the quota) and an O(1) approximation algorithms for the partial and budgeted versions of these problems. While the algorithms are simple, the results make a surprising use of the greedy set cover framework in defining a useful profit function.

Connected through hyperedge HE-1 (induced by source paper "an o log 2 n approximation algorithm for 2 edge connected dominating set"):
  with members:
    - "approximating k connected m dominating sets"
    - "approximation algorithm for minimum weight fault tolerant virtual backbone in unit disk graphs"
    - "nearly tight low stretch spanning trees"
    - "lower stretch spanning trees"

Connected through hyperedge HE-2 (induced by source paper "improved budgeted connected domination and budgeted edge vertex domination"):
  with members:
    - "maximum rooted connected expansion"

Connected through hyperedge HE-3 (induced by source paper "selective harvesting over networks"):
  with members:
    - "diversity promoting bayesian learning of latent variable models"
    - "building bridges viewing active learning from the multi armed bandit lens"
    - "the power of local information in social networks"
    - "bayesian optimal active search and surveying"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "importance weighted active learning"

Connected through hyperedge HE-4 (induced by source paper "distributed partial clustering"):
  with members:
    - "communication optimal distributed clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-5 (induced by source paper "approximation algorithms for connected maximum cut and related problems"):
  with members:
    - "a new perspective on vertex connectivity"
    - "deterministic parameterized connected vertex cover"
    - "submodular cost allocation problem and applications"

Connected through hyperedge HE-6 (induced by source paper "approximation algorithms for the connected sensor cover problem"):
  with members:
    - "a ptas for the weighted unit disk cover problem"
    - "analytical approach to parallel repetition"
    - "improved approximation algorithms for geometric set cover"

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)