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: "understanding edge connectivity in the internet through core decomposition"
  Abstract: Abstract The Internet is a complex network composed of several networks: the autonomous systems. Each of them is designed with the aim of transporting information efficiently. This information is carried over routes, which are discovered by routing protocols, such as the border gateway protocol (BGP). The protocols may find possible paths between nodes whenever they exist, or even find paths satisfying specific constraints, e.g., a certain quality of service (QoS). Here, we study connectivity as a network attribute related to both situations; we provide a formal lower bound to it based on core decomposition and low-complexity algorithms to find it. Then we apply these algorithms to analyze maps obtained from the prominent Internet mapping projects, and use the LaNet-vi open-source software for their visualization.

Connected through hyperedge HE-1 (induced by source paper "building large k cores from sparse graphs"):
  with members:
    - "a survey of parameterized algorithms and the complexity of edge modification"
    - "finding even subgraphs even faster"
    - "editing to a connected graph of given degrees"
    - "preventing unraveling in social networks gets harder"
    - "parameterized complexity of the anchored k core problem for directed graphs"
    - "editing to a graph of given degrees"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-2 (induced by source paper "efficient core maintenance in large dynamic graphs"):
  with members:
    - "distributed k core decomposition"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition a tool for the visualization of large scale networks"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"
    - "an o m algorithm for cores decomposition of 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.DM(Discrete Mathematics)