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: "secure mapreduce power iteration in the cloud"
  Abstract: With the development and wide deployment of web services, mobile applications, and sensor networks, data are now collected from many distributed sources to form big datasets. This paradigm poses a number of challenges on data storage and analysis. Typical data collectors have limited capacity to store and process large volumes of data and collected data may be highly sensitive requiring secure storage and processing. Processing and analyzing such large-scale data may also require a significant investment on the computing infrastructure which can be prohibitively expensive for many users. With these problems in mind, we envision a cloud-based data storage and processing framework that enables users to economically and securely handle big datasets. Specifically, we develop a cloud-based, MapReduce implementation of the power iteration algorithm in which the source matrix and intermediate computational values remain confidential. Our approach uses an iterative processing model in which the user interacts with the vast computing resources and encrypted data in the cloud until a converged solution is obtained. The security of this approach is guaranteed using Paillier encryption and a random perturbation technique. We carefully analyze its resilience to attacks. Our experimental results show that the proposed method is scalable to big matrices while requiring low client-side costs.

Connected through hyperedge HE-1 (induced by source paper "security and privacy aspects in mapreduce on clouds a survey"):
  with members:
    - "assignment problems of different sized inputs in mapreduce"
    - "computing marginals using mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"
    - "on the computational complexity of mapreduce"
    - "the family of mapreduce and large scale data processing systems"
    - "enumerating subgraph instances using map reduce"
    - "efficient processing of k nearest neighbor joins using mapreduce"
    - "efficient multi way theta join processing using mapreduce"

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)