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 "exploring neural models for parsing natural language into first order logic"):
  Abstract: Semantic parsing is the task of obtaining machine-interpretable representations from natural language text. We consider one such formal representation - First-Order Logic (FOL) and explore the capability of neural models in parsing English sentences to FOL. We model FOL parsing as a sequence to sequence mapping task where given a natural language sentence, it is encoded into an intermediate representation using an LSTM followed by a decoder which sequentially generates the predicates in the corresponding FOL formula. We improve the standard encoder-decoder model by introducing a variable alignment mechanism that enables it to align variables across predicates in the predicted FOL. We further show the effectiveness of predicting the category of FOL entity - Unary, Binary, Variables and Scoped Entities, at each decoder step as an auxiliary task on improving the consistency of generated FOL. We perform rigorous evaluations and extensive ablations. We also aim to release our code as well as large scale FOL dataset along with models to aid further research in logic-based parsing and inference in NLP.

Includes members:
  Member "probing neural network comprehension of natural language arguments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "seq2sql generating structured queries from natural language using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "semantic parsing with syntax and table aware sql generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs")
  Member "improving a neural semantic parser by counterfactual learning from human bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "found in translation reconstructing phylogenetic language trees from translations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic drift in multilingual representations")
  Member "learning a neural semantic parser from user feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "break it down a question understanding benchmark")
  Member "robust incremental neural semantic graph parsing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers")

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)