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: "temporal and spatial classification of active ipv6 addresses"
  Abstract: There is striking volume of World-Wide Web activity on IPv6 today. In early 2015, one large Content Distribution Network handles 50 billion IPv6 requests per day from hundreds of millions of IPv6 client addresses; billions of unique client addresses are observed per month. Address counts, however, obscure the number of hosts with IPv6 connectivity to the global Internet. There are numerous address assignment and subnetting options in use; privacy addresses and dynamic subnet pools significantly inflate the number of active IPv6 addresses. As the IPv6 address space is vast, it is infeasible to comprehensively probe every possible unicast IPv6 address. Thus, to survey the characteristics of IPv6 addressing, we perform a year-long passive measurement study, analyzing the IPv6 addresses gleaned from activity logs for all clients accessing a global CDN.   The goal of our work is to develop flexible classification and measurement methods for IPv6, motivated by the fact that its addresses are not merely more numerous; they are different in kind. We introduce the notion of classifying addresses and prefixes in two ways: (1) temporally, according to their instances of activity to discern which addresses can be considered stable; (2) spatially, according to the density or sparsity of aggregates in which active addresses reside. We present measurement and classification results numerically and visually that: provide details on IPv6 address use and structure in global operation across the past year; establish the efficacy of our classification methods; and demonstrate that such classification can clarify dimensions of the Internet that otherwise appear quite blurred by current IPv6 addressing practices.

Connected through hyperedge HE-1 (induced by source paper "discovering the ipv6 network periphery"):
  with members:
    - "eui 64 considered harmful"
    - "in the ip of the beholder strategies for active ipv6 topology discovery"
    - "clusters in the expanse understanding and unbiasing ipv6 hitlists"
    - "yarrp ing the internet randomized high speed active topology discovery"
    - "entropy ip uncovering structure in ipv6 addresses"

Connected through hyperedge HE-2 (induced by source paper "eui 64 considered harmful"):
  with members:
    - "in the ip of the beholder strategies for active ipv6 topology discovery"
    - "clusters in the expanse understanding and unbiasing ipv6 hitlists"

Connected through hyperedge HE-3 (induced by source paper "in the ip of the beholder strategies for active ipv6 topology discovery"):
  with members:
    - "clusters in the expanse understanding and unbiasing ipv6 hitlists"
    - "kip a measured approach to ipv6 address anonymization"
    - "yarrp ing the internet randomized high speed active topology discovery"
    - "entropy ip uncovering structure in ipv6 addresses"
    - "scanning the ipv6 internet towards a comprehensive hitlist"
    - "efficient algorithms for large scale topology discovery"

Connected through hyperedge HE-4 (induced by source paper "beyond counting new perspectives on the active ipv4 address space"):
  with members:
    - "a multi perspective analysis of carrier grade nat deployment"
    - "lost in space improving inference of ipv4 address space utilization"
    - "a primer on ipv4 scarcity"

Connected through hyperedge HE-5 (induced by source paper "entropy ip uncovering structure in ipv6 addresses"):
  with members:
    - "scanning the ipv6 internet towards a comprehensive hitlist"

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)