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: "data to text generation with entity modeling"
  Abstract: Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end. These models rely on representation learning to select content appropriately, structure it coherently, and verbalize it grammatically, treating entities as nothing more than vocabulary tokens. In this work we propose an entity-centric neural architecture for data-to-text generation. Our model creates entity-specific representations which are dynamically updated. Text is generated conditioned on the data input and entity memory representations using hierarchical attention at each time step. We present experiments on the RotoWire benchmark and a (five times larger) new dataset on the baseball domain which we create. Our results show that the proposed model outperforms competitive baselines in automatic and human evaluation.

Connected through hyperedge HE-1 (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"
    - "text generation with exemplar based adaptive decoding"
    - "handling divergent reference texts when evaluating table to text generation"
    - "calibration entropy rates and memory in language models"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "bottom up abstractive summarization"
    - "learning neural templates for text generation"

Connected through hyperedge HE-2 (induced by source paper "a hierarchical model for data to text generation"):
  with members:
    - "copy mechanism and tailored training for character based data to text generation"
    - "table2vec neural word and entity embeddings for table population and retrieval"
    - "tabvec table vectors for classification of web tables"
    - "learning neural templates for text generation"
    - "neural arithmetic logic units"
    - "a call for clarity in reporting bleu scores"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-3 (induced by source paper "revisiting challenges in data to text generation with fact grounding"):
  with members:
    - "learning to select track and generate for data to text"
    - "step by step separating planning from realization in neural data to text generation"
    - "end to end content and plan selection for data to text generation"
    - "describing a knowledge base"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "data to text generation with content selection and planning"
    - "learning to generate one sentence biographies from wikidata"
    - "challenges in data to document 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)