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: "greedy merge degrading has optimal power law"
  Abstract: Consider a channel with a given input distribution. Our aim is to degrade it to a channel with at most L output letters. One such degradation method is the so called "greedy-merge" algorithm. We derive an upper bound on the reduction in mutual information between input and output. For fixed input alphabet size and variable L, the upper bound is within a constant factor of an algorithm-independent lower bound. Thus, we establish that greedy-merge is optimal in the power-law sense.

Connected through hyperedge HE-1 (induced by source paper "locality sensitive hashing for f divergences mutual information loss and beyond"):
  with members:
    - "categorical feature compression via submodular optimization"
    - "norm ranging lsh for maximum inner product search"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "f divergence inequalities"
    - "a survey on learning to hash"
    - "hashing for similarity search a survey"
    - "a directed isoperimetric inequality with application to bregman near neighbor lower bounds"

Connected through hyperedge HE-2 (induced by source paper "information distilling quantizers"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "opening the black box of deep neural networks via information"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "on the construction of polar codes for channels with moderate input alphabet sizes"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "on the construction of polar codes"

Connected through hyperedge HE-3 (induced by source paper "information theoretical clustering is hard to approximate"):
  with members:
    - "approximation algorithms for clustering via weighted impurity measures"
    - "information distilling quantizers"
    - "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-4 (induced by source paper "approximation algorithms for clustering via weighted impurity measures"):
  with members:
    - "information distilling quantizers"
    - "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"

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)