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: "efficient on line schemes for encoding individual sequences with side information at the decoder"
  Abstract: We present adaptive on-line schemes for lossy encoding of individual sequences under the conditions of the Wyner-Ziv (WZ) problem. In the first part of this article, a set of fixed-rate scalar source codes with zero delay is presented. We propose a randomized on-line coding scheme, which achieves asymptotically (and with high probability), the performance of the best source code in the set, uniformly over all source sequences. The scheme uses the same rate and has zero delay. We then present an efficient algorithm for implementing our on-line coding scheme in the case of a relatively small set of encoders. We also present an efficient algorithm for the case of a larger set of encoders with a structure, using the method of the weighted graph and the Weight Pushing Algorithm (WPA). In the second part of this article, we extend our results to the case of variable-rate coding. A set of variable-rate scalar source codes is presented. We generalize the randomized on-line coding scheme, to our case. This time, the performance is measured by the Lagrangian Cost (LC), which is defined as a weighted sum of the distortion and the length of the encoded sequence. We present an efficient algorithm for implementing our on-line variable-rate coding scheme in the case of a relatively small set of encoders. We then consider the special case of lossless variable-rate coding. An on-line scheme which use Huffman codes is presented. We show that this scheme can be implemented efficiently using the same graphic methods from the first part. Combining the results from former sections, we build a generalized efficient algorithm for structured set of variable-rate encoders. The complexity of all the algorithms is no more than linear in the sequence length.

Connected through hyperedge HE-1 (induced by source paper "universal wyner ziv coding for distortion constrained general side information"):
  with members:
    - "channel coding and lossy source coding using a generator of constrained random numbers"
    - "tight exponential analysis of universally composable privacy amplification and its applications"
    - "achievability proof via output statistics of random binning"
    - "reliability in source coding with side information"
    - "rate distortion with side information at many decoders"
    - "the rate distortion function for product of two sources with side information at decoders"
    - "universal source coding over generalized complementary delivery networks"
    - "hash property and coding theorems for sparse matrices and maximum likelihood coding"

Connected through hyperedge HE-2 (induced by source paper "data processing bounds for scalar lossy source codes with side information at the decoder"):
  with members:
    - "structure theorems for real time variable rate coding with and without side information"
    - "data processing theorems and the second law of thermodynamics"
    - "lattice quantization with side information codes asymptotics and applications in sensor networks"

Connected through hyperedge HE-3 (induced by source paper "perfectly secure encryption of individual sequences"):
  with members:
    - "universal communication part i modulo additive channels"
    - "on the wyner ziv problem for individual sequences"
    - "on context tree prediction of individual sequences"

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)