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: "a note on the compaction of long training sequences for universal classification a non probabilistic approach"
  Abstract: One of the central problems in the classification of individual test sequences (e.g. genetic analysis), is that of checking for the similarity of sample test sequences as compared with a set of much longer training sequences. This is done by a set of classifiers for test sequences of length N, where each of the classifiers is trained by the training sequences so as to minimize the classification error rate when fed with each of the training sequences. #R##N#It should be noted that the storage of long training sequences is considered to be a serious bottleneck in the next generation sequencing for Genome analysis #R##N#Some popular classification algorithms adopt a probabilistic approach, by assuming that the sequences are realizations of some variable-length Markov process or a hidden Markov process (HMM), thus enabling the imbeding of the training data onto a variable-length Suffix-tree, the size of which is usually linear in $N$, the length of the test sequence. #R##N#Despite of the fact that it is not assumed here that the sequences are realizations of probabilistic processes (an assumption that does not seem to be fully justified when dealing with biological data), it is demonstrated that "feature-based" classifiers, where particular substrings (called "features" or markers) are sought in a set of "big data" training sequences may be based on a universal compaction of the training data that is contained in a set of $t$ (long) individual training sequences, onto a suffix-tree with no more than O(N) leaves, regardless of how long the training sequence is, at only a vanishing increase in the classification error rate.

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)