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: "when are tree structures necessary for deep learning of representations"
  Abstract: Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture. But there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In this paper we benchmark {\bf recursive} neural models against sequential {\bf recurrent} neural models (simple recurrent and LSTM models), enforcing apples-to-apples comparison as much as possible. We investigate 4 tasks: (1) sentiment classification at the sentence level and phrase level; (2) matching questions to answer-phrases; (3) discourse parsing; (4) semantic relation extraction (e.g., {\em component-whole} between nouns). #R##N#Our goal is to understand better when, and why, recursive models can outperform simpler models. We find that recursive models help mainly on tasks (like semantic relation extraction) that require associating headwords across a long distance, particularly on very long sequences. We then introduce a method for allowing recurrent models to achieve similar performance: breaking long sentences into clause-like units at punctuation and processing them separately before combining. Our results thus help understand the limitations of both classes of models, and suggest directions for improving recurrent models.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "extrapolation in nlp"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "a framework for end to end learning on semantic tree structured data"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "deep sets"
    - "learning to play general video games via an object embedding network"
    - "tree to tree neural networks for program translation"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "structural attention neural networks for improved sentiment analysis"
    - "abstract syntax networks for code generation and semantic parsing"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "multi scale self attention for text classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "star transformer"
    - "sequence labeling a practical approach"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations"):
  with members:
    - "towards robust neural machine translation"
    - "deep contextualized word representations"
    - "structural attention neural networks for improved sentiment analysis"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "chunk based bi scale decoder for neural machine translation"
    - "stronger baselines for trustable results in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "advcodec towards a unified framework for adversarial text generation"):
  with members:
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "textbugger generating adversarial text against real world applications"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "bp transformer modelling long range context via binary partitioning"):
  with members:
    - "hierarchical transformers for multi document summarization"
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "star transformer"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "generating wikipedia by summarizing long sequences"

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.AI(Artificial Intelligence)