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: "a modified ck secure sum protocol for multi party computation"
  Abstract: Abstract —Secure Multi-Party Computation (SMC) allows multiple parties to compute some function of their inputs without disclosing the actual inputs to one another. Secure sum computation is an easily understood example and the component of the various SMC solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a modified version of our ck-Secure Sum protocol with more security when a group of the computing parties conspire to know the data of some party. Index Terms —Privacy, Secure Multi-Party Computation, Secure Sum Computation, ck-Secure Sum Protocol. ——————————  —————————— 1 I NTRODUCTION The huge growth of the Internet and its easy access by common man created opportunities for joint computations by multiple parties. All the participating parties for the sake of their mutual benefit want to com-pute the common function of their inputs but at the same time they are worried about the privacy of their data. This subject of the information security is called Secure Multi-Party Computation (SMC). This subject has two goals; one is the privacy of the individual data inputs and another is the correctness of the result. Mainly two mod-els exist in the literature for the analysis of the SMC problems. Ideal model of the SMC uses a Trusted Third Party (TTP) apart from the participating parties. Parties supply their inputs to the TTP. Computation of the func-tion is done by the TTP and then the result is sent to all the parties. In this paradigm the trustworthiness of the TTP is critically important because if the TTP turns cor-rupt, it can supply the private inputs of one party to oth-ers. But it is extensively used model of the SMC due to its easy implementation and the protocols available which prevent the TTP to act maliciously. Real model of the SMC does not use any TTP but the parties themselves agree on some protocol for the computation. The party behavior in the SMC is important to consider. An honest party follows the protocol and respects the privacy of other parties. A semi honest party follows the protocol but also tries to learn other information than the result. The corrupt party neither follows the protocol nor re-spects the privacy of other parties. Different protocols are needed for different SMC models and the behavior of the party. Solutions are available for SMC problems using cryptographic techniques, randomization techniques and anonymization methods. The subject of SMC has been evolved from two party comparison problems [1] to mul-tiparty image template matching problems. Many specific SMC problems have been defined and analyzed by re-searchers like Private Information Retrieval (PIR), Selec-tive Function Evaluation, Privacy-Preserving Database Query, Privacy-Preserving Geometric Computation, Pri-vacy-Preserving Statistical Analysis, Privacy-Preserving Intrusion Detection and Privacy-Preserving Cooperative Scientific Computation. Based on these general SMC problems many real life applications emerged like Priva-cy-Preserving Electronic Voting, Privacy-Preserving Bid-ding and Auctions, Privacy-Preserving Social Network Analysis, Privacy-Preserving Signature and Face Detec-tion, etc. Secure sum computation problem of SMC can be de-fined as: How multiple parties can compute the sum of their input values without disclosing actual values to one another. Secure sum can work as the tool for the SMC solutions in the privacy preserving distributed data min-ing problems [2]. Clifton

Connected through hyperedge HE-1 (induced by source paper "simulation of collision resistant secure sum protocol"):
  with members:
    - "changing neighbors k secure sum protocol for secure multi party computation"
    - "a distributed k secure sum protocol for secure multi party computations"

Connected through hyperedge HE-2 (induced by source paper "a distributed k secure sum protocol for secure multi party computations"):
  with members:
    - "changing neighbors k secure sum protocol for secure multi party computation"
    - "privacy preserving k secure sum protocol"

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)