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: "cynical selection of language model training data"
  Abstract: The Moore-Lewis method of "intelligent selection of language model training data" is very effective, cheap, efficient... and also has structural problems. (1) The method defines relevance by playing language models trained on the in-domain and the out-of-domain (or data pool) corpora against each other. This powerful idea-- which we set out to preserve-- treats the two corpora as the opposing ends of a single spectrum. This lack of nuance does not allow for the two corpora to be very similar. In the extreme case where the come from the same distribution, all of the sentences have a Moore-Lewis score of zero, so there is no resulting ranking. (2) The selected sentences are not guaranteed to be able to model the in-domain data, nor to even cover the in-domain data. They are simply well-liked by the in-domain model; this is necessary, but not sufficient. (3) There is no way to tell what is the optimal number of sentences to select, short of picking various thresholds and building the systems. #R##N#We present a greedy, lazy, approximate, and generally efficient information-theoretic method of accomplishing the same goal using only vocabulary counts. The method has the following properties: (1) Is responsive to the extent to which two corpora differ. (2) Quickly reaches near-optimal vocabulary coverage. (3) Takes into account what has already been selected. (4) Does not involve defining any kind of domain, nor any kind of classifier. (6) Knows approximately when to stop. This method can be used as an inherently-meaningful measure of similarity, as it measures the bits of information to be gained by adding one text to another.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for domain adaptation in neural machine translation"):
  with members:
    - "data selection with cluster based language difference models and cynical selection"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "automated curriculum learning for neural networks"

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.CL(Computation and Language)