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: "multistep greedy algorithm identifies community structure in real world and computer generated networks"
  Abstract: We have recently introduced a multistep extension of the greedy algorithm for modularity optimization. The extension is based on the idea that merging l pairs of communities (l>1) at each iteration prevents premature condensation into few large communities. Here, an empirical formula is presented for the choice of the step width l that generates partitions with (close to) optimal modularity for 17 real-world and 1100 computer-generated networks. Furthermore, an in-depth analysis of the communities of two real-world networks (the metabolic network of the bacterium E. coli and the graph of coappearing words in the titles of papers coauthored by Martin Karplus) provides evidence that the partition obtained by the multistep greedy algorithm is superior to the one generated by the original greedy algorithm not only with respect to modularity, but also according to objective criteria. In other words, the multistep extension of the greedy algorithm reduces the danger of getting trapped in local optima of modularity and generates more reasonable partitions.

Connected through hyperedge HE-1 (induced by source paper "multiscale evolutionary perturbation attack on community detection"):
  with members:
    - "adversarial attacks on node embeddings via graph poisoning"
    - "ga based q attack on community detection"
    - "hiding individuals and communities in a social network"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "clustering and community detection in directed networks a survey"

Connected through hyperedge HE-2 (induced by source paper "metrics for community analysis a survey"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "higher order organization of complex networks"
    - "genperm a unified method for detecting non overlapping and overlapping communities"
    - "z score based modularity for community detection in networks"
    - "permanence and community structure in complex networks"
    - "generalized measures for the evaluation of community detection methods"
    - "generalization of clustering agreements and distances for overlapping clusters and network communities"

Connected through hyperedge HE-3 (induced by source paper "a framework for community detection in heterogeneous multi relational networks"):
  with members:
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "comparative evaluation of community detection algorithms a topological approach"
    - "an ant based algorithm with local optimization for community detection in large scale networks"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"

Connected through hyperedge HE-4 (induced by source paper "multi level algorithms for modularity clustering"):
  with members:
    - "modularity clustering is force directed layout"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"
    - "finding community structure in mega scale social networks"

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)