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: "k core decomposition a tool for the visualization of large scale networks"
  Abstract: We use the k-core decomposition to visualize large scale complex networks in two dimensions. This decomposition, based on a recursive pruning of the least connected vertices, allows to disentangle the hierarchical structure of networks by progressively focusing on their central cores. By using this strategy we develop a general visualization algorithm that can be used to compare the structural properties of various networks and highlight their hierarchical structure. The low computational complexity of the algorithm, O(n+e), where 'n' is the size of the network, and 'e' is the number of edges, makes it suitable for the visualization of very large sparse networks. We apply the proposed visualization tool to several real and synthetic graphs, showing its utility in finding specific structural fingerprints of computer generated and real world networks.

Connected through hyperedge HE-1 (induced by source paper "a socio informatic approach to automated account classification on social media"):
  with members:
    - "deploying south african social honeypots on twitter"
    - "a network topology approach to bot classification"
    - "online human bot interactions detection estimation and characterization"
    - "the darpa twitter bot challenge"
    - "dna inspired online behavioral modeling and its application to spambot detection"
    - "the rise of social bots"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "social turing tests crowdsourcing sybil detection"

Connected through hyperedge HE-2 (induced by source paper "parallel and streaming algorithms for k core decomposition"):
  with members:
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "snap a general purpose network analysis and graph mining library"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "parallel algorithms for geometric graph problems"
    - "densest subgraph in streaming and mapreduce"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "a hybrid approach for risk assessment of loan guarantee network"):
  with members:
    - "modeling the intensity function of point process via recurrent neural networks"
    - "xgboost a scalable tree boosting system"
    - "sales pipeline win propensity prediction a regression approach"
    - "the lifecycle and cascade of wechat social messaging groups"

Connected through hyperedge HE-4 (induced by source paper "enumerating k vertex connected components in large graphs"):
  with members:
    - "truss decomposition in massive networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-5 (induced by source paper "density friendly graph decomposition"):
  with members:
    - "a novel approach to finding near cliques the triangle densest subgraph problem"
    - "discovering nested communities"
    - "a model of internet topology using k shell decomposition"

Connected through hyperedge HE-6 (induced by source paper "i o efficient core graph decomposition at web scale"):
  with members:
    - "locally estimating core numbers"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-7 (induced by source paper "efficient core maintenance in large dynamic graphs"):
  with members:
    - "understanding edge connectivity in the internet through core decomposition"
    - "distributed k core decomposition"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-8 (induced by source paper "understanding edge connectivity in the internet through core decomposition"):
  with members:
    - "a model of internet topology using k shell decomposition"
    - "exploring networks with traceroute like probes theory and simulations"
    - "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.NI(Networking and Internet Architecture)