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: "multi level algorithms for modularity clustering"
  Abstract: Modularity is one of the most widely used quality measures for graph clusterings. Maximizing modularity is NP-hard, and the runtime of exact algorithms is prohibitive for large graphs. A simple and effective class of heuristics coarsens the graph by iteratively merging clusters (starting from singletons), and optionally refines the resulting clustering by iteratively moving individual vertices between clusters. Several heuristics of this type have been proposed in the literature, but little is known about their relative performance. #R##N#This paper experimentally compares existing and new coarsening- and refinement-based heuristics with respect to their effectiveness (achieved modularity) and efficiency (runtime). Concerning coarsening, it turns out that the most widely used criterion for merging clusters (modularity increase) is outperformed by other simple criteria, and that a recent algorithm by Schuetz and Caflisch is no improvement over simple greedy coarsening for these criteria. Concerning refinement, a new multi-level algorithm is shown to produce significantly better clusterings than conventional single-level algorithms. A comparison with published benchmark results and algorithm implementations shows that combinations of coarsening and multi-level refinement are competitive with the best algorithms in the literature.

Connected through hyperedge HE-1 (induced by source paper "persistent homology guided force directed graph layouts"):
  with members:
    - "visual detection of structural changes in time varying graphs using persistent homology"

Connected through hyperedge HE-2 (induced by source paper "correlation based community detection"):
  with members:
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "higher order organization of complex networks"
    - "permanence and community structure in complex networks"
    - "heat kernel based community detection"
    - "a testing based extraction algorithm for identifying significant communities in networks"
    - "defining and evaluating network communities based on ground truth"
    - "empirical comparison of algorithms for network community detection"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"

Connected through hyperedge HE-3 (induced by source paper "network community detection with a successive spectral relaxation method"):
  with members:
    - "finding community structure in mega scale social networks"

Connected through hyperedge HE-4 (induced by source paper "engineering multilevel support vector machines"):
  with members:
    - "a pso and pattern search based memetic algorithm for svms parameters optimization"
    - "a divide and conquer solver for kernel support vector machines"
    - "advanced coarsening schemes for graph partitioning"
    - "fast response to infection spread and cyber attacks on large scale networks"
    - "relaxation based coarsening and multiscale graph organization"
    - "multiscale approach for the network compression friendly ordering"

Connected through hyperedge HE-5 (induced by source paper "driving interactive graph exploration using 0 dimensional persistent homology features"):
  with members:
    - "visual detection of structural changes in time varying graphs using persistent homology"

Connected through hyperedge HE-6 (induced by source paper "router level community structure of the internet autonomous systems"):
  with members:
    - "uncovering the small community structure in large networks a local spectral approach"
    - "graph spectra and the detectability of community structure in networks"
    - "on weakly optimal partitions in modular networks"
    - "exploring networks with traceroute like probes theory and simulations"

Connected through hyperedge HE-7 (induced by source paper "surprise maximization reveals the community structure of complex networks"):
  with members:
    - "qualitative comparison of community detection algorithms"
    - "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"

Connected through hyperedge HE-8 (induced by source paper "graph based approaches to clustering network constrained trajectory data"):
  with members:
    - "a tutorial on spectral clustering"

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)