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: "distance based and continuum fano inequalities with applications to statistical estimation"
  Abstract: In this technical note, we give two extensions of the classical Fano inequality in information theory. The first extends Fano's inequality to the setting of estimation, providing lower bounds on the probability that an estimator of a discrete quantity is within some distance $t$ of the quantity. The second inequality extends our bound to a continuum setting and provides a volume-based bound. We illustrate how these inequalities lead to direct and simple proofs of several statistical minimax lower bounds.

Connected through hyperedge HE-1 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation"):
  with members:
    - "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "worst case vs average case design for estimation from fixed pairwise comparisons"
    - "an adaptive sublinear time block sparse fourier transform"
    - "lower bounds on active learning for graphical model selection"
    - "f divergence inequalities"
    - "on the difficulty of selecting ising models with approximate recovery"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"

Connected through hyperedge HE-2 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "lower bounds for compressed sensing with generative models"
    - "solving inverse problems via auto encoders"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "the universal approximation power of finite width deep relu networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-3 (induced by source paper "inference under information constraints i lower bounds from chi square contraction"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "two party distribution testing communication and security"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "a general characterization of the statistical query complexity"

Connected through hyperedge HE-4 (induced by source paper "geometric lower bounds for distributed parameter estimation under communication constraints"):
  with members:
    - "distributed simulation and distributed inference"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "distributed learning communication complexity and privacy"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-5 (induced by source paper "authentication of cyber physical systems under learning based attacks"):
  with members:
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "dynamic watermarking active defense of networked cyber physical systems"
    - "networked control under random and malicious packet losses"
    - "divergence based characterization of fundamental limitations of adaptive dynamical systems"

Connected through hyperedge HE-6 (induced by source paper "principal inertia components and applications"):
  with members:
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "the most informative boolean function conjecture holds for high noise"
    - "bounds between contraction coefficients"
    - "on the duality of additivity and tensorization"
    - "managing your private and public data bringing down inference attacks against your privacy"
    - "remarks on the most informative function conjecture at fixed mean"
    - "euclidean information theory of networks"
    - "notes on information theoretic privacy"

Connected through hyperedge HE-7 (induced by source paper "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"):
  with members:
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "how to escape saddle points efficiently"
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "low rank approximation with entrywise ell_1 norm error"

Connected through hyperedge HE-8 (induced by source paper "on fundamental limits of robust learning"):
  with members:
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "optimality guarantees for distributed statistical estimation"
    - "distributed learning communication complexity and privacy"

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.IT(Information Theory)