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 structured composition in neural networks without tree structured architectures"
  Abstract: Tree-structured neural networks encode a particular tree geometry for a sentence in the network design. However, these models have at best only slightly outperformed simpler sequence-based models. We hypothesize that neural sequence models like LSTMs are in fact able to discover and implicitly use recursive compositional structure, at least for tasks with clear cues to that structure in the data. We demonstrate this possibility using an artificial data task for which recursive compositional structure is crucial, and find an LSTM-based sequence model can indeed learn to exploit the underlying tree structure. However, its performance consistently lags behind that of tree models, even on large training sets, suggesting that tree-structured models are more effective at exploiting recursive structure.

Connected through hyperedge HE-1 (induced by source paper "synonymous generalization in sequence to sequence recurrent networks"):
  with members:
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "memorize or generalize searching for a compositional rnn in a haystack"
    - "jump to better conclusions scan both left and right"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "building machines that learn and think like people"
    - "tensorflow a system for large scale machine learning"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"

Connected through hyperedge HE-3 (induced by source paper "ordered memory"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "cooperative learning of disjoint syntax and semantics"
    - "listops a diagnostic dataset for latent tree learning"
    - "improved sentence modeling using suffix bidirectional lstm"
    - "systematic generalization what is required and can it be learned"
    - "deep contextualized word representations"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-4 (induced by source paper "towards better modeling hierarchical structure for self attention with ordered neurons"):
  with members:
    - "exploiting sentential context for neural machine translation"
    - "transformers with convolutional context for asr"
    - "self attention with structural position representations"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "exploiting deep representations for neural machine translation"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "does bert agree evaluating knowledge of structure dependence through agreement relations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "what does bert look at an analysis of bert s attention"
    - "marrying universal dependencies and universal morphology"
    - "analysis methods in neural language processing a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "a logical based corpus for cross lingual evaluation"):
  with members:
    - "assessing bert s syntactic abilities"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "dialogue natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "can neural networks understand logical entailment"

Connected through hyperedge HE-7 (induced by source paper "a unifying framework of bilinear lstms"):
  with members:
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "targeted syntactic evaluation of language models"
    - "end to end neural coreference resolution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "regularizing and optimizing lstm language models"
    - "hadamard product for low rank bilinear pooling"
    - "xception deep learning with depthwise separable convolutions"
    - "on multiplicative integration with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "unity in diversity learning distributed heterogeneous sentence representation for extractive summarization"):
  with members:
    - "hybrid memnet for extractive summarization"
    - "neural summarization by extracting sentences and words"
    - "neural tree indexers for text understanding"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)