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: "exponential strong converse for content identification with lossy recovery"
  Abstract: We revisit the high-dimensional content identification with lossy recovery problem (Tuncel and G\"und\"uz, 2014) and establish an exponential strong converse theorem. As a corollary of the exponential strong converse theorem, we derive an upper bound on the joint identification-error and excess-distortion exponent for the problem. Our main results can be specialized to the biometrical identification problem~(Willems, 2003) and the content identification problem~(Tuncel, 2009) since these two problems are both special cases of the content identification with lossy recovery problem. We leverage the information spectrum method introduced by Oohama and adapt the strong converse techniques therein to be applicable to the problem at hand.

Connected through hyperedge HE-1 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information"):
  with members:
    - "strong converse using change of measure arguments"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "new strong converse for asymmetric broadcast channels"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "strong successive refinability and rate distortion complexity tradeoff"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "on cooperative multiple access channels with delayed csi at transmitters"

Connected through hyperedge HE-2 (induced by source paper "resolution limits of noisy 20 questions estimation"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "improved target acquisition rates with feedback codes"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "the dispersion of nearest neighbor decoding for additive non gaussian channels"
    - "second order coding rates for channels with state"
    - "searching with measurement dependent noise"
    - "collaborative 20 questions for target localization"

Connected through hyperedge HE-3 (induced by source paper "dispersion bound for the wyner ahlswede k orner network via reverse hypercontractivity on types"):
  with members:
    - "strong converse using change of measure arguments"
    - "information theoretic perspectives on brascamp lieb inequality and its reverse"
    - "a converse bound on wyner ahlswede k orner network via gray wyner network"
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "asymptotic estimates in information theory with non vanishing error probabilities"

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)