human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "table to text natural language generation with unseen schemas"):
  Abstract: Traditional table-to-text natural language generation (NLG) tasks focus on generating text from schemas that are already seen in the training set. This limitation curbs their generalizabilities towards real-world scenarios, where the schemas of input tables are potentially infinite. In this paper, we propose the new task of table-to-text NLG with unseen schemas, which specifically aims to test the generalization of NLG for input tables with attribute types that never appear during training. To do this, we construct a new benchmark dataset for this task. To deal with the problem of unseen attribute types, we propose a new model that first aligns unseen table schemas to seen ones, and then generates text with updated table representations. Experimental evaluation on the new benchmark demonstrates that our model outperforms baseline methods by a large margin. In addition, comparison with standard data-to-text settings shows the challenges and uniqueness of our proposed task.

Includes members:
  Member "key fact as pivot a two stage model for low resource table to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational template machine for data to text generation")
  Member "domain adaptive dialog generation via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "domain adaptation of neural machine translation by lexicon induction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised domain adaptation for neural machine translation with iterative back translation")
  Member "step by step separating planning from realization in neural data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "paraphrase generation with latent bag of words")
  Member "learning to compose topic aware mixture of experts for zero shot video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "delving deeper into the decoder for video captioning")
  Member "zero shot question generation from knowledge graphs for unseen predicates and entity types":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "natural language to structured query generation via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised learning")
  Member "data to text generation with content selection and planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "paraphrase generation with latent bag of words")

Question: Please tell me which class the source paper 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)