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: "worst case optimal adaptive prefix coding"
  Abstract: A common complaint about adaptive prefix coding is that it is much slower than static prefix coding. Karpinski and Nekrich recently took an important step towards resolving this: they gave an adaptive Shannon coding algorithm that encodes each character in (O (1)) amortized time and decodes it in (O (\log H)) amortized time, where $H$ is the empirical entropy of the input string $s$. For comparison, Gagie's adaptive Shannon coder and both Knuth's and Vitter's adaptive Huffman coders all use (\Theta (H)) amortized time for each character. In this paper we give an adaptive Shannon coder that both encodes and decodes each character in (O (1)) worst-case time. As with both previous adaptive Shannon coders, we store $s$ in at most ((H + 1) |s| + o (|s|)) bits. We also show that this encoding length is worst-case optimal up to the lower order term.

Connected through hyperedge HE-1 (induced by source paper "efficient and compact representations of prefix codes"):
  with members:
    - "new lower and upper bounds for representing sequences"
    - "on compressing permutations and adaptive sorting"
    - "fixed block compression boosting in fm indexes"
    - "compressed representations of permutations and applications"
    - "fast and compact prefix codes"
    - "dynamic asymmetric communication"
    - "large alphabets and incompressibility"
    - "time space trade offs for predecessor search"

Connected through hyperedge HE-2 (induced by source paper "efficient fully compressed sequence representations"):
  with members:
    - "optimal dynamic sequence representations"
    - "new lower and upper bounds for representing sequences"
    - "on compressing permutations and adaptive sorting"
    - "succinct representations of permutations and functions"
    - "optimal trade off for succinct string indexes"
    - "compressed representations of permutations and applications"
    - "more haste less waste lowering the redundancy in fully indexable dictionaries"
    - "a lower bound for succinct rank queries"

Connected through hyperedge HE-3 (induced by source paper "fast and compact prefix codes"):
  with members:
    - "dynamic asymmetric communication"
    - "large alphabets and incompressibility"

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)