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 k nearest neighbor query over encrypted data in outsourced environments"
  Abstract: For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.

Connected through hyperedge HE-1 (induced by source paper "dynamic skyline queries on encrypted data using result materialization"):
  with members:
    - "secure and efficient skyline queries on encrypted data"
    - "towards practical oblivious ram"

Connected through hyperedge HE-2 (induced by source paper "sanns scaling up secure approximate k nearest neighbors search"):
  with members:
    - "scalable nearest neighbor search for optimal transport"
    - "approximate nearest neighbor search in high dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "scalable secure computation of statistical functions with applications to k nearest neighbors"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "secure k ish nearest neighbors classifier"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for oblivious near neighbor search"):
  with members:
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "hardness of approximate nearest neighbor search"
    - "scalable secure computation of statistical functions with applications to k nearest neighbors"
    - "lower bounds for oblivious data structures"
    - "distributed pcp theorems for hardness of approximation in p"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "new unconditional hardness results for dynamic and online problems"
    - "the cell probe complexity of dynamic range counting"

Connected through hyperedge HE-4 (induced by source paper "sub linear privacy preserving near neighbor search"):
  with members:
    - "lower bounds for oblivious near neighbor search"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "fair near neighbor search independent range sampling in high dimensions"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "exploring privacy preservation in outsourced k nearest neighbors with multiple data owners"
    - "privacy via the johnson lindenstrauss transform"
    - "coding for random projections"
    - "a simple and practical algorithm for differentially private data release"

Connected through hyperedge HE-5 (induced by source paper "lptd achieving lightweight and privacy preserving truth discovery in ciot"):
  with members:
    - "truth finding on the deep web is the problem solved"
    - "cognitive internet of things a new paradigm beyond connection"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving ddos attack detection using cross domain traffic in software defined networks"):
  with members:
    - "privacy preserving cross domain routing optimization a cryptographic approach"

Connected through hyperedge HE-7 (induced by source paper "passbio privacy preserving user centric biometric authentication"):
  with members:
    - "secure fingerprint alignment and matching protocols"
    - "expsos secure and verifiable outsourcing of exponentiation operations for mobile cloud computing"
    - "secure biometrics concepts authentication architectures and challenges"

Connected through hyperedge HE-8 (induced by source paper "sub linear privacy preserving search with untrusted server and semi honest parties"):
  with members:
    - "riposte an anonymous messaging system handling millions of users"
    - "exploring privacy preservation in outsourced k nearest neighbors with multiple data owners"
    - "privacy via the johnson lindenstrauss transform"
    - "coding for random projections"
    - "iterative hard thresholding for compressed sensing"

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)