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: "enhancing sentence embedding with generalized pooling"
  Abstract: Pooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding. We propose vector-based multi-head attention that includes the widely used max pooling, mean pooling, and scalar self-attention as special cases. The model benefits from properly designed penalization terms to reduce redundancy in multi-head attention. We evaluate the proposed model on three different tasks: natural language inference (NLI), author profiling, and sentiment classification. The experiments show that the proposed model achieves significant improvement over strong sentence-encoding-based methods, resulting in state-of-the-art performances on four datasets. The proposed approach can be easily implemented for more problems than we discuss in this paper.

Connected through hyperedge HE-1 (induced by source paper "sequential neural networks for noetic end to end response selection"):
  with members:
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "sequential attention based network for noetic end to end response selection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "conditional self attention for query based summarization"):
  with members:
    - "universal transformers"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "reinforced mnemonic reader for machine comprehension"
    - "neural natural language inference models enhanced with external knowledge"

Connected through hyperedge HE-3 (induced by source paper "textnas a neural architecture search space tailored for text representation"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "deep contextualized word representations"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "natural language inference with hierarchical bilstm max pooling architecture"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-4 (induced by source paper "sentence embeddings in nli with iterative refinement encoders"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "testing the generalization power of neural network models across nli benchmarks"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "sequential attention based network for noetic end to end response selection"):
  with members:
    - "modeling multi turn conversation with deep utterance aggregation"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "a structured self attentive sentence embedding"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what if we simply swap the two text fragments a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks"
    - "stress test evaluation for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "multi level matching and aggregation network for few shot relation classification"):
  with members:
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "one shot relational learning for knowledge graphs"
    - "natural language inference over interaction space"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "cell aware stacked lstms for modeling sentences"):
  with members:
    - "on tree based neural sentence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "learning to generate reviews and discovering sentiment"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep learning with dynamic computation graphs"
    - "fast slow 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)