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 "content based table retrieval for web queries"):
  Abstract: Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing. In this work, we focus on content-based table retrieval. Given a query, the task is to find the most relevant table from a collection of tables. Further progress towards improving this area requires powerful models of semantic matching and richer training and evaluation resources. To remedy this, we present a ranking based approach, and implement both carefully designed features and neural network architectures to measure the relevance between a query and the content of a table. Furthermore, we release an open-domain dataset that includes 21,113 web queries for 273,816 tables. We conduct comprehensive experiments on both real world and synthetic datasets. Results verify the effectiveness of our approach and present the challenges for this task.

Includes members:
  Member "neural text generation from structured data with application to the biography domain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational template machine for data to text generation")
  Member "end to end memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "neural programmer inducing latent programs with gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "what to talk about and how selective generation using lstms with coarse to fine alignment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational template machine for data to text generation")
  Member "neural generative question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator")
  Member "neural enquirer learning to query tables with natural language":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a translate edit model for natural language question to sql query generation on multi relational healthcare data")
  Member "compositional semantic parsing on semi structured tables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "empirical evaluation of gated recurrent neural networks on sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)