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: "long short term memory over tree structures"
  Abstract: The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process. We call the model S-LSTM, which provides a principled way of considering long-distance interaction over hierarchies, e.g., language or image parse structures. We leverage the models for semantic composition to understand the meaning of text, a fundamental problem in natural language understanding, and show that it outperforms a state-of-the-art recursive model by replacing its composition layers with the S-LSTM memory blocks. We also show that utilizing the given structures is helpful in achieving a performance better than that without considering the structures.

Connected through hyperedge HE-1 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-2 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "unsupervised recurrent neural network grammars"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "text generation with exemplar based adaptive decoding"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"

Connected through hyperedge HE-3 (induced by source paper "tree transformer a transformer based method for correction of tree structured data"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "automatic software repair a bibliography"
    - "tree2tree neural translation model for learning source code changes"
    - "learning to repair software vulnerabilities with generative adversarial networks"
    - "tree to tree neural networks for program translation"
    - "semantic code repair using neuro symbolic transformation networks"
    - "a nested attention neural hybrid model for grammatical error correction"

Connected through hyperedge HE-4 (induced by source paper "dense recurrent neural networks for scene labeling"):
  with members:
    - "multi context attention for human pose estimation"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "scene parsing with global context embedding"
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "interpretable structure evolving lstm"):
  with members:
    - "recurrent topic transition gan for visual paragraph generation"
    - "semantic object parsing with graph lstm"
    - "pixel recurrent neural networks"
    - "semantic object parsing with local global long short term memory"
    - "joint object and part segmentation using deep learned potentials"
    - "conditional random fields as recurrent neural networks"
    - "zoom better to see clearer human part segmentation with auto zoom net"
    - "attention to scale scale aware semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "tree memory networks for modelling long term temporal dependencies"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "interpretable structure evolving lstm"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "semantic object parsing with graph lstm"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "the lifted matrix space model for semantic composition"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "natural language inference over interaction space"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "neural semantic encoders"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "compositional distributional semantics with long short term memory"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "tree structured neural machine for linguistics aware sentence generation"):
  with members:
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "improved semantic representations from tree structured long short term memory networks"
    - "top down tree long short term memory networks"
    - "neural responding machine for short text conversation"
    - "a neural conversational model"

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)