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: "regular expressions for decoding of neural network outputs"
  Abstract: Abstract   This article proposes a convenient tool for decoding the output of neural networks trained by Connectionist Temporal Classification (CTC) for handwritten text recognition. We use regular expressions to describe the complex structures expected in the writing. The corresponding finite automata are employed to build a decoder. We analyze theoretically which calculations are relevant and which can be avoided. A great speed-up results from an approximation. We conclude that the approximation most likely fails if the regular expression does not match the ground truth which is not harmful for many applications since the low probability will be even underestimated. The proposed decoder is very efficient compared to other decoding methods. The variety of applications reaches from information retrieval to full text recognition. We refer to applications where we integrated the proposed decoder successfully.

Connected through hyperedge HE-1 (induced by source paper "a two stage method for text line detection in historical documents"):
  with members:
    - "baseline detection in historical documents using convolutional u nets"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "multi task handwritten document layout analysis"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "full page text recognition learning where to start and when to stop"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"

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"
    - "symbolic priors for rnn based semantic parsing"
    - "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"

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:
    - "symbolic priors for rnn based semantic parsing"
    - "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"

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.NE(Neural and Evolutionary Computing)