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 "zero shot transfer learning for semantic parsing"):
  Abstract: While neural networks have shown impressive performance on large datasets, applying these models to tasks where little data is available remains a challenging problem. #R##N#In this paper we propose to use feature transfer in a zero-shot experimental setting on the task of semantic parsing. #R##N#We first introduce a new method for learning the shared space between multiple domains based on the prediction of the domain label for each example. #R##N#Our experiments support the superiority of this method in a zero-shot experimental setting in terms of accuracy metrics compared to state-of-the-art techniques. #R##N#In the second part of this paper we study the impact of individual domains and examples on semantic parsing performance. #R##N#We use influence functions to this aim and investigate the sensitivity of domain-label classification loss on each example. #R##N#Our findings reveal that cross-domain adversarial attacks identify useful examples for training even from the domains the least similar to the target domain. Augmenting our training data with these influential examples further boosts our accuracy at both the token and the sequence level.

Includes members:
  Member "seq2sql generating structured queries from natural language using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "sqlnet generating structured queries from natural language without reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model")
  Member "zero shot learning a comprehensive evaluation of the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "neural semantic parsing over multiple knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic parsing with dual learning")
  Member "transfer learning for neural semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatialnli a spatial domain natural language interface to databases using spatial comprehension")
  Member "cross domain semantic parsing via paraphrasing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "paraphrase generation with latent bag of words")
  Member "language to logical form with neural attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised question decomposition for question answering")
  Member "matching networks for one shot learning":
    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)