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: "differentially private iterative synchronous consensus"
  Abstract: The iterative consensus problem requires a set of processes or agents with different initial values, to interact and update their states to eventually converge to a common value. Protocols solving iterative consensus serve as building blocks in a variety of systems where distributed coordination is required for load balancing, data aggregation, sensor fusion, filtering, clock synchronization and platooning of autonomous vehicles. In this paper, we introduce the private iterative consensus problem where agents are required to converge while protecting the privacy of their initial values from honest but curious adversaries. Protecting the initial states, in many applications, suffice to protect all subsequent states of the individual participants. #R##N#First, we adapt the notion of differential privacy in this setting of iterative computation. Next, we present a server-based and a completely distributed randomized mechanism for solving private iterative consensus with adversaries who can observe the messages as well as the internal states of the server and a subset of the clients. Finally, we establish the tradeoff between privacy and the accuracy of the proposed randomized mechanism.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving distributed machine learning via local randomization and admm perturbation"):
  with members:
    - "optimal differentially private admm for distributed machine learning"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "robust decentralized learning using admm with unreliable agents"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "distributed representations of sentences and documents"
    - "collaborative deep learning for recommender systems"
    - "building confidential and efficient query services in the cloud with rasp data perturbation"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "a system theoretic framework for privacy preservation in continuous time multiagent dynamics"):
  with members:
    - "dynamical privacy in distributed computing part i privacy loss and ppsc mechanism"
    - "a dynamical approach to privacy preserving average consensus"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "secure and privacy preserving average consensus"
    - "adaptive communication networks with privacy guarantees"

Connected through hyperedge HE-3 (induced by source paper "bidirectional information flow and the roles of privacy masks in cloud based control"):
  with members:
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "finite state channels with time invariant deterministic feedback"

Connected through hyperedge HE-4 (induced by source paper "statistical privacy in distributed average consensus on bounded real inputs"):
  with members:
    - "secure and privacy preserving average consensus"
    - "private learning on networks"

Connected through hyperedge HE-5 (induced by source paper "resilient consensus for multi agent systems subject to differential privacy requirements"):
  with members:
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "resilient randomized quantized consensus"
    - "r robustness and r s robustness of circulant graphs"
    - "exploiting nodes symmetries to control synchronization and consensus patterns in multiagent systems"
    - "a jamming resilient algorithm for self triggered network coordination"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "differentially private distributed optimization"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-6 (induced by source paper "on the relationship between inference and data privacy in decentralized iot networks"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "learning in hierarchical social networks"
    - "privacy against statistical inference"
    - "the value of feedback in decentralized detection"

Connected through hyperedge HE-7 (induced by source paper "dynamics based privacy protection for average consensus on directed graphs"):
  with members:
    - "secure and privacy preserving average consensus"
    - "adaptive communication networks with privacy guarantees"
    - "private and accurate data aggregation against dishonest nodes"
    - "differentially private distributed optimization"

Connected through hyperedge HE-8 (induced by source paper "a dynamical approach to privacy preserving average consensus"):
  with members:
    - "dynamical privacy in distributed computing part i privacy loss and ppsc mechanism"
    - "secure and privacy preserving average consensus"
    - "adaptive communication networks with privacy guarantees"

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)