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: "fast multi scale community detection based on local criteria within a multi threaded algorithm"
  Abstract: Many systems can be described using graphs, or networks. Detecting communities in these networks can provide information about the underlying structure and functioning of the original systems. Yet this detection is a complex task and a large amount of work was dedicated to it in the past decade. One important feature is that communities can be found at several scales, or levels of resolution, indicating several levels of organisations. Therefore solutions to the community structure may not be unique. Also networks tend to be large and hence require ecient processing. In this work, we present a new algorithm for the fast detection of communities across scales using a local criterion. We exploit the local aspect of the criterion to enable parallel computation and improve the algorithm’s eciency further. The algorithm is tested against large generated multi-scale networks and experiments demonstrate its eciency and accuracy.

Connected through hyperedge HE-1 (induced by source paper "centrality measures for networks with community structure"):
  with members:
    - "community based immunization strategies for epidemic control"
    - "nonlinear spread of rumor and inoculation strategies in the nodes with degree dependent tie strength in complex networks"
    - "comparative evaluation of community detection algorithms a topological approach"
    - "mapping the gnutella network properties of large scale peer to peer systems and implications for system design"

Connected through hyperedge HE-2 (induced by source paper "uncovering nodes that spread information between communities in social networks"):
  with members:
    - "a large scale community structure analysis in facebook"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)