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: "developing security reputation metrics for hosting providers"
  Abstract: Research into cybercrime often points to concentrations of abuse at certain hosting providers. The implication is that these providers are worse in terms of security; some are considered `bad' or even `bullet proof'. Remarkably little work exists on systematically comparing the security performance of providers. Existing metrics typically count instances of abuse and sometimes normalize these counts by taking into account the advertised address space of the provider. None of these attempts have worked through the serious methodological challenges that plague metric design. In this paper we present a systematic approach for metrics development and identify the main challenges: (i) identification of providers, (ii) abuse data coverage and quality, (iii) normalization, (iv) aggregation and (v) metric interpretation. We describe a pragmatic approach to deal with these challenges. In the process, we answer an urgent question posed to us by the Dutch police: `which are the worst providers in our jurisdiction?'. Notwithstanding their limitations, there is a clear need for security metrics for hosting providers in the fight against cybercrime.

Connected through hyperedge HE-1 (induced by source paper "herding vulnerable cats a statistical approach to disentangle joint responsibility for web security in shared hosting"):
  with members:
    - "two novel server side attacks against log file in shared web hosting servers"
    - "rotten apples or bad harvest what we are measuring when we are measuring abuse"

Connected through hyperedge HE-2 (induced by source paper "rotten apples or bad harvest what we are measuring when we are measuring abuse"):
  with members:
    - "revealing the autonomous system taxonomy the machine learning approach"

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.CR(Cryptography and Security)