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: "prio private robust and scalable computation of aggregate statistics"
  Abstract: This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics. Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location). As long as at least one server is honest, the Prio servers learn nearly nothing about the clients' private data, except what they can infer from the aggregate statistics that the system computes. To protect functionality in the face of faulty or malicious clients, Prio uses secret-shared non-interactive proofs (SNIPs), a new cryptographic technique that yields a hundred-fold performance improvement over conventional zero-knowledge approaches. Prio extends classic private aggregation techniques to enable the collection of a large class of useful statistics. For example, Prio can perform a least-squares regression on high-dimensional client-provided data without ever seeing the data in the clear.

Connected through hyperedge HE-1 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation"):
  with members:
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "that which we call private"
    - "private stochastic convex optimization with optimal rates"
    - "evaluating differentially private machine learning in practice"
    - "differentially private summation with multi message shuffling"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"

Connected through hyperedge HE-2 (induced by source paper "timecrypt encrypted data stream processing at scale with cryptographic access control"):
  with members:
    - "jedi many to many end to end encryption and key delegation for iot"
    - "streetx spatio temporal access control model for data"
    - "sok cryptographically protected database search"
    - "efficient private statistics with succinct sketches"

Connected through hyperedge HE-3 (induced by source paper "secure federated submodel learning"):
  with members:
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "deep leakage from gradients"
    - "towards federated learning at scale system design"
    - "making ai forget you data deletion in machine learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated machine learning concept and applications"
    - "consistent and accurate frequency oracles under local differential privacy"

Connected through hyperedge HE-4 (induced by source paper "cryptflow secure tensorflow inference"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "a hybrid approach to secure function evaluation using sgx"
    - "helen maliciously secure coopetitive learning for linear models"
    - "trident efficient 4pc framework for privacy preserving machine learning"
    - "astra high throughput 3pc over rings with application to secure prediction"
    - "xonn xnor based oblivious deep neural network inference"

Connected through hyperedge HE-5 (induced by source paper "practical and robust privacy amplification with multi party differential privacy"):
  with members:
    - "outis crypto assisted differential privacy on untrusted servers"
    - "separating local shuffled differential privacy via histograms"
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "secure and utility aware data collection with condensed local differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "an algorithmic framework for differentially private data analysis on trusted processors"

Connected through hyperedge HE-6 (induced by source paper "helen maliciously secure coopetitive learning for linear models"):
  with members:
    - "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "spectre attacks exploiting speculative execution"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "deepsecure scalable provably secure deep learning"

Connected through hyperedge HE-7 (induced by source paper "private aggregation from fewer anonymous messages"):
  with members:
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated learning strategies for improving communication efficiency"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "distributed differential privacy via shuffling"

Connected through hyperedge HE-8 (induced by source paper "express lowering the cost of metadata hiding communication with cryptographic privacy"):
  with members:
    - "xrd scalable messaging system with cryptographic privacy"
    - "the loopix anonymity system"
    - "atom horizontally scaling strong anonymity"
    - "riposte an anonymous messaging system handling millions of users"
    - "conscript your friends into larger anonymity sets with javascript"
    - "proactively accountable anonymous messaging in verdict"

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)