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: "quantifying surveillance in the networked age node based intrusions and group privacy"
  Abstract: From the "right to be left alone" to the "right to selective disclosure", privacy has long been thought as the control individuals have over the information they share and reveal about themselves. However, in a world that is more connected than ever, the choices of the people we interact with increasingly affect our privacy. This forces us to rethink our definition of privacy. We here formalize and study, as local and global node- and edge-observability, Bloustein's concept of group privacy. We prove edge-observability to be independent of the graph structure, while node-observability depends only on the degree distribution of the graph. We show on synthetic datasets that, for attacks spanning several hops such as those implemented by social networks and current US laws, the presence of hubs increases node-observability while a high clustering coefficient decreases it, at fixed density. We then study the edge-observability of a large real-world mobile phone dataset over a month and show that, even under the restricted two-hops rule, compromising as little as 1% of the nodes leads to observing up to 46% of all communications in the network. More worrisome, we also show that on average 36\% of each person's communications would be locally edge-observable under the same rule. Finally, we use real sensing data to show how people living in cities are vulnerable to distributed node-observability attacks. Using a smartphone app to compromise 1\% of the population, an attacker could monitor the location of more than half of London's population. Taken together, our results show that the current individual-centric approach to privacy and data protection does not encompass the realities of modern life. This makes us---as a society---vulnerable to large-scale surveillance attacks which we need to develop protections against.

Connected through hyperedge HE-1 (induced by source paper "dynamically protecting privacy under uncertainty"):
  with members:
    - "query complexity of bayesian private learning"
    - "private sequential learning"
    - "perception games and privacy"
    - "riposte an anonymous messaging system handling millions of users"
    - "spy vs spy rumor source obfuscation"
    - "the empirical implications of privacy aware choice"
    - "privacy preserving public information for sequential games"
    - "robust adaptive routing under uncertainty"

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.CY(Computers and Society)