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: "symbolic priors for rnn based semantic parsing"
  Abstract: Seq2seq models based on Recurrent Neural Networks (RNNs) have recently received a lot of attention in the domain of Semantic Parsing for Question Answering. While in principle they can be trained directly on pairs (natural language utterances, logical forms), their performance is limited by the amount of available data. To alleviate this problem, we propose to exploit various sources of prior knowledge: the well-formedness of the logical forms is modeled by a weighted context-free grammar; the likelihood that certain entities present in the input utterance are also present in the logical form is modeled by weighted finite-state automata. The grammar and automata are combined together through an efficient intersection algorithm to form a soft guide ("background") to the RNN. We test our method on an extension of the Overnight dataset and show that it not only strongly improves over an RNN baseline, but also outperforms non-RNN models based on rich sets of hand-crafted features.

Connected through hyperedge HE-1 (induced by source paper "grammatical sequence prediction for real time neural semantic parsing"):
  with members:
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "vocabulary selection strategies for neural machine translation"
    - "a syntactic neural model for general purpose code generation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "log linear rnns towards recurrent neural networks with flexible prior knowledge"

Connected through hyperedge HE-2 (induced by source paper "generalize symbolic knowledge with neural rule engine"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "marrying up regular expressions with neural networks a case study for spoken language understanding"
    - "object oriented neural programming oonp for document understanding"
    - "advances in pre training distributed word representations"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "harnessing deep neural networks with logic rules"
    - "enriching word vectors with subword information"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-3 (induced by source paper "marrying up regular expressions with neural networks a case study for spoken language understanding"):
  with members:
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "learning to remember rare events"
    - "harnessing deep neural networks with logic rules"
    - "disturblabel regularizing cnn on the loss layer"
    - "lifted rule injection for relation embeddings"
    - "matching networks for one shot learning"
    - "neural generation of regular expressions from natural language with minimal domain knowledge"
    - "regular expressions for decoding of neural network outputs"

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)