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: "root oram a tunable differentially private oblivious ram"
  Abstract: State-of-the-art mechanisms for oblivious RAM (ORAM) suffer from significant bandwidth overheads (greater than 100x) that impact the throughput and latency of memory accesses. This renders their deployment in high-performance and bandwidth-constrained applications difficult, motivating the design of low-overhead approaches for memory access obfuscation. In this work, we introduce and formalize the notion of a differentially private ORAM that provides statistical privacy guarantees, and which to the extent of our knowledge, is the first of its kind. The formalization of differentially private ORAM opens up a large design space of low-bandwidth ORAM protocols that can be deployed in bandwidth constrained applications. We present Root ORAM, a family of practical ORAMs that provide a tunable, multi-dimensional trade-off between the desired bandwidth overhead, outsourcing ratio (ratio of the data outsourced to local storage required) and the system security, and that provide rigorous privacy guarantees of differentially private ORAMs. Root ORAM protocols can be tuned to achieve application-specific bandwidth constraints, enabling practical deployment, at the cost of statistical privacy guarantees quantified under the differential privacy framework and lower outsourcing ratios. We demonstrate the practicality of Root ORAM using theoretical analysis, simulations, as well as experiments on Amazon EC2. Our theoretical analysis rigorously quantifies the privacy offered by Root ORAM, and provably bounds the information leaked from observing memory access patterns. Our experimental analysis shows the feasibility of these protocols using realistic simulations. The simplest protocol in the Root ORAM family requires a bandwidth of mere 10 blocks, at the cost of rigorously quantified security loss and a low outsourcing ratio. This is an order of magnitude improvement over the existing state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "what storage access privacy is achievable with small overhead"):
  with members:
    - "accessing data while preserving privacy"
    - "differentially private oblivious ram"
    - "the melbourne shuffle improving oblivious storage in the cloud"
    - "calibrating data to sensitivity in private data analysis"
    - "privacy preserving access of outsourced data via oblivious ram simulation"

Connected through hyperedge HE-2 (induced by source paper "an algorithmic framework for differentially private data analysis on trusted processors"):
  with members:
    - "the privacy blanket of the shuffle model"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "distributed differential privacy via shuffling"
    - "practical locally private heavy hitters"
    - "cachezoom how sgx amplifies the power of cache attacks"
    - "software grand exposure sgx cache attacks are practical"
    - "towards practical differential privacy for sql queries"
    - "prochlo strong privacy for analytics in the crowd"

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)