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: "tree recurrent neural networks with application to language modeling"
  Abstract: In this paper we develop a recurrent neural network (TreeRNN), which is designed to predict a tree rather than a linear sequence as is the case in conventional recurrent neural networks. Our model defines the probability of a sentence by estimating the generation probability of its dependency tree. We construct the tree incrementally by generating the left and right dependents of a node whose probability is computed using recurrent neural networks with shared hidden layers. Application of our model to two language modeling tasks shows that it outperforms or performs on par with related models.

Connected through hyperedge HE-1 (induced by source paper "searching a database of source codes using contextualized code search"):
  with members:
    - "multi modal attention network learning for semantic source code retrieval"
    - "on learning meaningful code changes via neural machine translation"
    - "when deep learning met code search"
    - "mapping language to code in programmatic context"
    - "tree2tree neural translation model for learning source code changes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "top down tree structured decoding with syntactic connections for neural machine translation and parsing"
    - "robustfill neural program learning under noisy i o"

Connected through hyperedge HE-2 (induced by source paper "deep tree transductions a short survey"):
  with members:
    - "text summarization as tree transduction by top down treelstm"
    - "hidden tree markov networks deep and wide learning for structured data"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "sentence compression as tree transduction"

Connected through hyperedge HE-3 (induced by source paper "top down tree structured decoding with syntactic connections for neural machine translation and parsing"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "towards string to tree neural machine translation"
    - "tree to sequence attentional neural machine translation"
    - "language to logical form with neural attention"
    - "recurrent neural network grammars"
    - "syntactically guided neural machine translation"
    - "linguistic input features improve neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "text summarization as tree transduction by top down treelstm"):
  with members:
    - "hidden tree markov networks deep and wide learning for structured data"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "end to end memory networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"
    - "sentence compression as tree transduction"

Connected through hyperedge HE-5 (induced by source paper "a multi level convolutional lstm model for the segmentation of left ventricle myocardium in infarcted porcine cine mr images"):
  with members:
    - "direct estimation of regional wall thicknesses via residual recurrent neural network"
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "semantic object parsing with local global long short term memory"
    - "deep residual learning for image recognition"
    - "top down tree long short term memory networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "reformulating level sets as deep recurrent neural network approach to semantic segmentation"):
  with members:
    - "learning to segment object candidates"
    - "instance aware semantic segmentation via multi task network cascades"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-7 (induced by source paper "easy first dependency parsing with hierarchical tree lstms"):
  with members:
    - "recurrent neural network grammars"
    - "skip thought vectors"
    - "transition based dependency parsing with stack long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "top down tree long short term memory networks"
    - "a re ranking model for dependency parser with recursive convolutional neural network"
    - "neural crf parsing"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-8 (induced by source paper "recurrent dropout without memory loss"):
  with members:
    - "character aware neural language models"
    - "transition based dependency parsing with stack long short term memory"
    - "visualizing and understanding recurrent networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "recurrent neural network regularization"
    - "learning longer memory in recurrent neural networks"

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)