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: "given enough eyeballs all bugs are shallow revisiting eric raymond with bug bounty programs"
  Abstract: Author(s): Maillart, Thomas; Zhao, Mingyi; Grossklags, Jens; Chuang, John | Abstract: Bug bounty programs offer a modern way for organizations to crowdsource their software security, and for security researchers to be fairly rewarded for the vulnerabilities they find. However, little is known on the incentives set by bug bounty programs – how they drive engagement and new bug discoveries. This article provides an empirical investigation of the strategic interactions among the managers and participants of bug bounty programs, as well as the intermediation by bug bounty platforms. We find that for a given bug bounty program, each security researcher can only expect to discover a bounded number of bugs. This result offers a validation step to a theory brought forth early on by Brady et al. This theory proposes that each security researcher inspecting a piece of software offers a unique environment of skills and mindset, which is amenable to the discovery of bugs that others may not be able to uncover. Bug bounty programs indeed benefit from the engagement of large crowds of researchers. Conversely, security researchers benefit greatly from searching for bugs in multiple bug bounty programs. However, we find that following a strong front-loading effect, newly launched programs attract researchers at the expense of older programs: the probability of finding bugs decays as ∼1/t0.4∼1/t0.4 after the launch of a program, even though bugs found later yield on average higher rewards. Our results lead us to formulate three recommendations for organizing bug bounty programs and platforms: (i) organize enrollment, mobility, and renewal of security researchers across bounty programs, (ii) highlight and organize programs for front-loading, and (iii) organize fluid market transactions to reduce uncertainty and thus reduce incentives for security researchers to sell on the black market.

Connected through hyperedge HE-1 (induced by source paper "closing the ai accountability gap defining an end to end framework for internal algorithmic auditing"):
  with members:
    - "principles alone cannot guarantee ethical ai"
    - "principles alone cannot guarantee ethical ai"
    - "diversity in faces"
    - "model cards for model reporting"
    - "evolutionary computation and ai safety research problems impeding routine and safe real world application of evolution"
    - "the global landscape of ai ethics guidelines"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a broader view on bias in automated decision making reflecting on epistemology and dynamics"

Connected through hyperedge HE-2 (induced by source paper "aristotle vs ringelmann on superlinear production in open source software"):
  with members:
    - "aristotle vs ringelmann a response to scholtes et al on superlinear production in open source software"
    - "using prediction markets to incentivize and measure collective knowledge production"
    - "coase s penguin or linux and the nature of the firm"

Connected through hyperedge HE-3 (induced by source paper "bounties in open source development on github a case study of bountysource bounties"):
  with members:
    - "understanding the heterogeneity of contributors in bug bounty programs"

Connected through hyperedge HE-4 (induced by source paper "hey google what exactly do your security patches tell us a large scale empirical study on android patched vulnerabilities"):
  with members:
    - "an economic study of the effect of android platform fragmentation on security updates"

Connected through hyperedge HE-5 (induced by source paper "a bug bounty perspective on the disclosure of web vulnerabilities"):
  with members:
    - "herding vulnerable cats a statistical approach to disentangle joint responsibility for web security in shared hosting"
    - "classifying web exploits with topic modeling"
    - "understanding the heterogeneity of contributors in bug bounty programs"

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)