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: "carrier grade anomaly detection using time to live header information"
  Abstract: Time-to-Live data in the IP header offers two interesting characteristics: First, different IP stacks pick different start TTL values. Second, each traversed router should decrement the TTL value. The combination of both offers host and route fingerprinting options. We present the first work to investigate Internet-wide TTL behavior at carrier scale and evaluate its fit to detect anomalies, predominantly spoofed source IP addresses. Using purpose-built software, we capture 2 weeks of raw TTL data at a 40 Gbit/s Internet uplink. For further insight, we actively measure observed hosts and conduct large-scale hitlist-based measurements, which yields three complementary data sets for IPv4 and IPv6. A majority (69% IPv4; 81% IPv6) of passively observed multi-packet hosts exhibit one stable TTL value. Active measurements on unstable hosts yield a stable anchor TTL value for more than 85% of responsive hosts. We develop a structure to further classify unstable hosts taking, for example, temporal stability into account. Correlation of TTL values with BGP data is clear, yet unpredictive. The results indicate that carrier-grade TTL anomaly detection can yield significant insights in the following categories: First, the method can flag anomalies based on TTL observations (yet likely at a difficult false positive/false negative trade-off). Second, the method can establish trust that a packet originates from its acclaimed source.

Connected through hyperedge HE-1 (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"
    - "entropy ip uncovering structure in ipv6 addresses"
    - "towards better internet citizenship reducing the footprint of internet wide scans by topology aware prefix selection"
    - "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)