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 "neural multi step reasoning for question answering on semi structured tables"):
  Abstract: Advances in natural language processing tasks have gained momentum in recent years due to the increasingly popular neural network methods. In this paper, we explore deep learning techniques for answering multi-step reasoning questions that operate on semi-structured tables. Challenges here arise from the level of logical compositionality expressed by questions, as well as the domain openness. Our approach is weakly supervised, trained on question-answer-table triples without requiring intermediate strong supervision. It performs two phases: first, machine understandable logical forms (programs) are generated from natural language questions following the work of [Pasupat and Liang, 2015]. Second, paraphrases of logical forms and questions are embedded in a jointly learned vector space using word and character convolutional neural networks. A neural scoring function is further used to rank and retrieve the most probable logical form (interpretation) of a question. Our best single model achieves 34.8% accuracy on the WikiTableQuestions dataset, while the best ensemble of our models pushes the state-of-the-art score on this task to 38.7%, thus slightly surpassing both the engineered feature scoring baseline, as well as the Neural Programmer model of [Neelakantan et al., 2016].

Includes members:
  Member "learning a natural language interface with neural programmer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plotqa reasoning over scientific plots")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "neural enquirer learning to query tables with natural language":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a translate edit model for natural language question to sql query generation on multi relational healthcare data")
  Member "character aware neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trends and advancements in deep neural network communication")
  Member "compositional semantic parsing on semi structured tables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "fast and accurate deep network learning by exponential linear units elus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "open question answering with weakly supervised embedding models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural graph embedding methods for natural language processing")

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)