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: "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement"
  Abstract: Identifying strongly connected substructures in large networks provides insight into their coarse-grained organization. Several approaches based on the optimization of a quality function, e.g., the modularity, have been proposed. We present here a multistep extension of the greedy algorithm (MSG) that allows the merging of more than one pair of communities at each iteration step. The essential idea is to prevent the premature condensation into few large communities. Upon convergence of the MSG a simple refinement procedure called "vertex mover" (VM) is used for reassigning vertices to neighboring communities to improve the final modularity value. With an appropriate choice of the step width, the combined MSG-VM algorithm is able to find solutions of higher modularity than those reported previously. The multistep extension does not alter the scaling of computational cost of the greedy algorithm.

Connected through hyperedge HE-1 (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"
    - "multi level algorithms for modularity clustering"

Connected through hyperedge HE-2 (induced by source paper "community detection through likelihood optimization in search of a sound model"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "network structure exploration via bayesian nonparametric models"
    - "clustering and community detection in directed networks a survey"
    - "model of complex networks based on citation dynamics"

Connected through hyperedge HE-3 (induced by source paper "label propagation for clustering"):
  with members:
    - "corrected overlap weight and clustering coefficient"
    - "the many facets of community detection in complex networks"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"
    - "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"
    - "group detection in complex networks an algorithm and comparison of the state of the art"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-4 (induced by source paper "network structure exploration via bayesian nonparametric models"):
  with members:
    - "overlapping community detection in networks with positive and negative links"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "finding missing edges in networks based on their community structure"
    - "nonparametric link prediction in dynamic networks"
    - "an infinite latent attribute model for network data"
    - "experience versus talent shapes the structure of the web"

Connected through hyperedge HE-5 (induced by source paper "clustering evolving networks"):
  with members:
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "benchmarking community detection methods on social media data"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "on graph stream clustering with side information"
    - "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments"
    - "detect overlapping and hierarchical community structure in networks"
    - "a tutorial on spectral clustering"
    - "on the np completeness of some graph cluster measures"

Connected through hyperedge HE-6 (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"
    - "multistep greedy algorithm identifies community structure in real world and computer generated networks"

Connected through hyperedge HE-7 (induced by source paper "labelrank a stabilized label propagation algorithm for community detection in networks"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "towards linear time overlapping community detection in social networks"
    - "community detection using a neighborhood strength driven label propagation algorithm"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "finding community structure in mega scale social networks"

Connected through hyperedge HE-8 (induced by source paper "surprise maximization reveals the community structure of complex networks"):
  with members:
    - "qualitative comparison of community detection algorithms"
    - "multi level algorithms for modularity 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)