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: "calibration entropy rates and memory in language models"
  Abstract: Building accurate language models that capture meaningful long-term dependencies is a core challenge in natural language processing. Towards this end, we present a calibration-based approach to measure long-term discrepancies between a generative sequence model and the true distribution, and use these discrepancies to improve the model. Empirically, we show that state-of-the-art language models, including LSTMs and Transformers, are \emph{miscalibrated}: the entropy rates of their generations drift dramatically upward over time. We then provide provable methods to mitigate this phenomenon. Furthermore, we show how this calibration-based approach can also be used to measure the amount of memory that language models use for prediction.

Connected through hyperedge HE-1 (induced by source paper "on a class of markov order estimators based on ppm and other universal codes"):
  with members:
    - "scaling laws for neural language models"
    - "regular hilberg processes an example of processes with a vanishing entropy rate"
    - "mixing ergodic and nonergodic processes with rapidly growing information between blocks"
    - "estimating the entropy of binary time series methodology some theory and a simulation study"

Connected through hyperedge HE-2 (induced by source paper "sticking to the facts confident decoding for faithful data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "data to text generation with entity modeling"
    - "text generation with exemplar based adaptive decoding"
    - "handling divergent reference texts when evaluating table to text generation"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "bottom up abstractive summarization"
    - "learning neural templates for text generation"

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)