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: "data compression with low distortion and finite blocklength"
  Abstract: This paper considers lossy source coding of $n$-dimensional memoryless sources and shows an explicit approximation to the minimum source coding rate required to sustain the probability of exceeding distortion $d$ no greater than $\epsilon$, which is simpler than known dispersion-based approximations. #R##N#Our approach takes inspiration in the celebrated classical result stating that the Shannon lower bound to rate-distortion function becomes tight in the limit $d \to 0$. We formulate an abstract version of the Shannon lower bound that recovers both the classical Shannon lower bound and the rate-distortion function itself as special cases. Likewise, we show that a nonasymptotic version of the abstract Shannon lower bound recovers all previously known nonasymptotic converses. #R##N#A necessary and sufficient condition for the Shannon lower bound to be attained exactly is presented. It is demonstrated that whenever that condition is met, the rate-dispersion function is given simply by the varentropy of the source. Remarkably, all finite alphabet sources with balanced distortion measures satisfy that condition in the range of low distortions. #R##N#Most continuous sources violate that condition. Still, we show that lattice quantizers closely approach the nonasymptotic Shannon lower bound, provided that the source density is smooth enough and the distortion is low. This implies that fine multidimensional lattice coverings are nearly optimal in the rate-distortion sense even at finite $n$. The achievability proof technique is based on a new bound on the output entropy of lattice quantizers in terms of the differential entropy of the source, the lattice cell size and a smoothness parameter of the source density. The technique avoids both the usual random coding argument and the simplifying assumption of the presence of a dither signal.

Connected through hyperedge HE-1 (induced by source paper "a lower bound on the differential entropy of log concave random vectors with applications"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "wasserstein stability of the entropy power inequality for log concave densities"
    - "the shannon lower bound is asymptotically tight"
    - "links between the logarithmic sobolev inequality and the convolution inequalities for entropy and fisher information"
    - "a concavity property for the reciprocal of fisher information and its consequences on costa s epi"
    - "entropy bounds on abelian groups and the ruzsa divergence"
    - "a strengthened entropy power inequality for log concave densities"
    - "the entropy per coordinate of a random vector is highly constrained under convexity conditions"

Connected through hyperedge HE-2 (induced by source paper "successive refinement of abstract sources"):
  with members:
    - "strong successive refinability and rate distortion complexity tradeoff"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-3 (induced by source paper "rate cost tradeoffs in control"):
  with members:
    - "an upper bound to zero delay rate distortion via kalman filtering for vector gaussian sources"
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "a coding theorem for a class of stationary channels with feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-4 (induced by source paper "the shannon lower bound is asymptotically tight"):
  with members:
    - "degrees of freedom in vector interference channels"
    - "rate distortion bounds for high resolution vector quantization via gibbs s inequality"

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)