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 nonstochastic information theory for communication and state estimation"
  Abstract: In communications, unknown variables are usually modelled as random variables, and concepts such as independence, entropy and information are defined in terms of the underlying probability distributions. In contrast, control theory often treats uncertainties and disturbances as bounded unknowns having no statistical structure. The area of networked control combines both fields, raising the question of whether it is possible to construct meaningful analogues of stochastic concepts such as independence, Markovness, entropy and information without assuming a probability space. This paper introduces a framework for doing so, leading to the construction of a maximin information functional for nonstochastic variables. It is shown that the largest maximin information rate through a memoryless, error-prone channel in this framework coincides with the block-coding zero-error capacity of the channel. Maximin information is then used to derive tight conditions for uniformly estimating the state of a linear time-invariant system over such a channel, paralleling recent results of Matveev and Savkin.

Connected through hyperedge HE-1 (induced by source paper "a signal space distance measure for nondispersive optical fiber"):
  with members:
    - "towards a non stochastic information theory"
    - "autocorrelation function for dispersion free fiber channels with distributed amplification"
    - "fast numerical nonlinear fourier transforms"
    - "upper bound on the capacity of the nonlinear schr odinger channel"
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "design of apsk constellations for coherent optical channels with nonlinear phase noise"
    - "optimizing constellations for single subcarrier intensity modulated optical systems"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-2 (induced by source paper "noiseless privacy"):
  with members:
    - "an operational approach to information leakage"
    - "uncertain wiretap channels and secure estimation"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "the composition theorem for differential privacy"
    - "information masking and amplification the source coding setting"
    - "privacy against statistical inference"

Connected through hyperedge HE-3 (induced by source paper "developing non stochastic privacy preserving policies using agglomerative clustering"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "a submodularity based agglomerative clustering algorithm for the privacy funnel"
    - "tunable measures for information leakage and applications to privacy utility tradeoffs"
    - "privacy preserving prediction"
    - "uncertain wiretap channels and secure estimation"
    - "notes on information theoretic privacy"
    - "from the information bottleneck to the privacy funnel"
    - "privacy against statistical inference"

Connected through hyperedge HE-4 (induced by source paper "development and analysis of deterministic privacy preserving policies using non stochastic information theory"):
  with members:
    - "uncertain wiretap channels and secure estimation"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "information masking and amplification the source coding setting"
    - "the optimal mechanism in differential privacy"
    - "privacy against statistical inference"
    - "on the relation between differential privacy and quantitative information flow"

Connected through hyperedge HE-5 (induced by source paper "non stochastic hypothesis testing with application to privacy against hypothesis testing adversary"):
  with members:
    - "issues encountered deploying differential privacy"
    - "uncertain wiretap channels and secure estimation"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"

Connected through hyperedge HE-6 (induced by source paper "worst case guarantees for remote estimation of an uncertain source"):
  with members:
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

Connected through hyperedge HE-7 (induced by source paper "zero error capacity of multiple access channels via nonstochastic information"):
  with members:
    - "a vc dimension based outer bound on the zero error capacity of the binary adder channel"

Connected through hyperedge HE-8 (induced by source paper "feedback capacity over networks"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"

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.SY(Systems and Control)