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: "efficient algorithms for large scale topology discovery"
  Abstract: There is a growing interest in discovery of internet topology at the interface level. A new generation of highly distributed measurement systems is currently being deployed. Unfortunately, the research community has not examined the problem of how to perform such measurements efficiently and in a network-friendly manner. In this paper we make two contributions toward that end. First, we show that standard topology discovery methods (e.g., skitter) are quite inefficient, repeatedly probing the same interfaces. This is a concern, because when scaled up, such methods will generate so much traffic that they will begin to resemble DDoS attacks. We measure two kinds of redundancy in probing (intra- and inter-monitor) and show that both kinds are important. We show that straightforward approaches to addressing these two kinds of redundancy must take opposite tacks, and are thus fundamentally in conflict. Our second contribution is to propose and evaluate Doubletree, an algorithm that reduces both types of redundancy simultaneously on routers and end systems. The key ideas are to exploit the tree-like structure of routes to and from a single point in order to guide when to stop probing, and to probe each path by starting near its midpoint. Our results show that Doubletree can reduce both types of measurement load on the network dramatically, while permitting discovery of nearly the same set of nodes and links. We then show how to enable efficient communication between monitors through the use of Bloom filters.

Connected through hyperedge HE-1 (induced by source paper "tnt watch me explode a light in the dark for revealing mpls tunnels"):
  with members:
    - "yarrp ing the internet randomized high speed active topology discovery"

Connected through hyperedge HE-2 (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"
    - "temporal and spatial classification of active ipv6 addresses"

Connected through hyperedge HE-3 (induced by source paper "occam an optimization based approach to network inference"):
  with members:
    - "topology discovery of sparse random graphs with few participants"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-4 (induced by source paper "a radar for the internet"):
  with members:
    - "fast dynamics in internet topology preliminary observations and explanations"
    - "detection understanding and prevention of traceroute measurement artifacts"
    - "exploring networks with traceroute like probes theory and simulations"
    - "efficient route tracing from a single source"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-5 (induced by source paper "implementation and deployment of a distributed network topology discovery algorithm"):
  with members:
    - "dimes let the internet measure itself"

Connected through hyperedge HE-6 (induced by source paper "efficient route tracing from a single source"):
  with members:
    - "dimes let the internet measure itself"

Connected through hyperedge HE-7 (induced by source paper "dimes let the internet measure itself"):
  with members:
    - "k core decomposition a tool for the visualization of large scale 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)