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: "information distilling quantizers"
  Abstract: Let $X$ and $Y$ be dependent random variables. This paper considers the problem of designing a scalar quantizer for $Y$ to maximize the mutual information between the quantizer's output and $X$, and develops fundamental properties and bounds for this form of quantization, which is connected to the log-loss distortion criterion. The main focus is the regime of low $I(X;Y)$, where it is shown that, if $X$ is binary, a constant fraction of the mutual information can always be preserved using $\mathcal{O}(\log(1/I(X;Y)))$ quantization levels, and there exist distributions for which this many quantization levels are necessary. Furthermore, for larger finite alphabets $2 < |\mathcal{X}| < \infty$, it is established that an $\eta$-fraction of the mutual information can be preserved using roughly $(\log(| \mathcal{X} | /I(X;Y)))^{\eta\cdot(|\mathcal{X}| - 1)}$ quantization levels.

Connected through hyperedge HE-1 (induced by source paper "information theoretical clustering is hard to approximate"):
  with members:
    - "approximation algorithms for clustering via weighted impurity measures"
    - "greedy merge degrading has optimal power law"
    - "the hardness of approximation of euclidean k means"
    - "channel upgradation for non binary input alphabets and macs"
    - "how to construct polar codes"
    - "quantization of binary input discrete memoryless channels"
    - "approximation algorithms for bregman co clustering and tensor clustering"
    - "k means requires exponentially many iterations even in the plane"

Connected through hyperedge HE-2 (induced by source paper "approximation algorithms for clustering via weighted impurity measures"):
  with members:
    - "greedy merge degrading has optimal power law"
    - "the hardness of approximation of euclidean k means"
    - "channel upgradation for non binary input alphabets and macs"
    - "how to construct polar codes"
    - "quantization of binary input discrete memoryless channels"
    - "approximation algorithms for bregman co clustering and tensor clustering"

Connected through hyperedge HE-3 (induced by source paper "greedy merge degrading has optimal power law"):
  with members:
    - "construction of polar codes for arbitrary discrete memoryless channels"
    - "on the construction of polar codes for channels with moderate input alphabet sizes"
    - "channel upgradation for non binary input alphabets and macs"
    - "on the construction of polar codes"
    - "at low snr asymmetric quantizers are better"
    - "how to construct polar codes"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "quantization of binary input discrete memoryless channels"

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)