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: "entropy ip uncovering structure in ipv6 addresses"
  Abstract: In this paper, we introduce Entropy/IP: a system that discovers Internet address structure based on analyses of a subset of IPv6 addresses known to be active, i.e., training data, gleaned by readily available passive and active means. The system is completely automated and employs a combination of information-theoretic and machine learning techniques to probabilistically model IPv6 addresses. We present results showing that our system is effective in exposing structural characteristics of portions of the active IPv6 Internet address space, populated by clients, services, and routers.   In addition to visualizing the address structure for exploration, the system uses its models to generate candidate addresses for scanning. For each of 15 evaluated datasets, we train on 1K addresses and generate 1M candidates for scanning. We achieve some success in 14 datasets, finding up to 40% of the generated addresses to be active. In 11 of these datasets, we find active network identifiers (e.g., /64 prefixes or "subnets") not seen in training. Thus, we provide the first evidence that it is practical to discover subnets and hosts by scanning probabilistically selected areas of the IPv6 address space not known to contain active hosts a priori.

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"
    - "temporal and spatial classification of active ipv6 addresses"

Connected through hyperedge HE-2 (induced by source paper "clusters in the expanse understanding and unbiasing ipv6 hitlists"):
  with members:
    - "in the ip of the beholder strategies for active ipv6 topology discovery"
    - "kip a measured approach to ipv6 address anonymization"
    - "hloc hints based geolocation leveraging multiple measurement frameworks"
    - "large scale classification of ipv6 ipv4 siblings with variable clock skew"
    - "beyond counting new perspectives on the active ipv4 address space"
    - "towards better internet citizenship reducing the footprint of internet wide scans by topology aware prefix selection"
    - "scanning the ipv6 internet towards a comprehensive hitlist"
    - "carrier grade anomaly detection using time to live header information"

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"
    - "scanning the ipv6 internet towards a comprehensive hitlist"
    - "temporal and spatial classification of active ipv6 addresses"
    - "efficient algorithms for large scale topology discovery"

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)