human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "look up and adapt a one shot semantic parser"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "generating sentences by editing prototypes"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "neural semantic parsing over multiple knowledge bases"
    - "cross domain semantic parsing via paraphrasing"
    - "language to logical form with neural attention"
    - "achieving human parity in conversational speech recognition"

Question: Please tell me which class the center node 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)