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: "graph annotations in modeling complex network topologies"
  Abstract: The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in such a graph possess some nontrivial internal structure that varies across and differentiates among distinct types of links or nodes. In this work, we abstract such additional information as network annotations. We introduce a network topology modeling framework that treats annotations as an extended correlation profile of a network. Assuming we have this profile measured for a given network, we present an algorithm to rescale it in order to construct networks of varying size that still reproduce the original measured annotation profile.   Using this methodology, we accurately capture the network properties essential for realistic simulations of network applications and protocols, or any other simulations involving complex network topologies, including modeling and simulation of network evolution. We apply our approach to the Autonomous System (AS) topology of the Internet annotated with business relationships between ASs. This topology captures the large-scale structure of the Internet. In depth understanding of this structure and tools to model it are cornerstones of research on future Internet architectures and designs. We find that our techniques are able to accurately capture the structure of annotation correlations within this topology, thus reproducing a number of its important properties in synthetically-generated random graphs.

Connected through hyperedge HE-1 (induced by source paper "on the privacy of dk random graphs"):
  with members:
    - "an efficient and robust social network de anonymization attack"
    - "privacy games optimal user centric data obfuscation"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"
    - "an efficient reconciliation algorithm for social networks"
    - "de anonymizing social networks"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-2 (induced by source paper "privacy in social media identification mitigation and applications"):
  with members:
    - "social media and user privacy"
    - "similar but different exploiting users congruity for recommendation systems"
    - "a survey of location prediction on twitter"
    - "securing social media user data an adversarial approach"
    - "blind de anonymization attacks using social networks"
    - "walk2friends inferring social links from mobility profiles"
    - "on content based recommendation and user privacy in social tagging systems"
    - "identifying community structures in dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "construction of directed 2k graphs"):
  with members:
    - "exact sampling of graphs with prescribed degree correlations"
    - "graphic realizations of joint degree matrices"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-4 (induced by source paper "performance rescaling of complex networks"):
  with members:
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-5 (induced by source paper "efficient proactive caching for supporting seamless mobility"):
  with members:
    - "femtocaching wireless video content delivery through distributed caching helpers"

Connected through hyperedge HE-6 (induced by source paper "evolution of the internet as level ecosystem"):
  with members:
    - "an integrated model of traffic geography and economy in the internet"
    - "chinese internet as level topology"
    - "as relationships inference and validation"
    - "the workshop on internet topology wit report"
    - "systematic topology analysis and generation using degree correlations"
    - "the internet as level topology three data sources and one definitive metric"
    - "understanding the evolution dynamics of internet topology"
    - "revealing the autonomous system taxonomy the machine learning approach"

Connected through hyperedge HE-7 (induced by source paper "time series of internet as level topology graphs four patterns and one model"):
  with members:
    - "the workshop on internet topology wit report"
    - "systematic topology analysis and generation using degree correlations"
    - "the internet as level topology three data sources and one definitive metric"
    - "lessons from three views of the internet topology"
    - "accurately modeling the internet topology"

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)