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: "learning universal sentence representations with mean max attention autoencoder"
  Abstract: In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max attention autoencoder (mean-max AAE) within the encoder-decoder framework. Our autoencoder rely entirely on the MultiHead self-attention mechanism to reconstruct the input sequence. In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input. To enable the information to steer the reconstruction process dynamically, the decoder performs attention over the mean-max representation. By training our model on a large collection of unlabelled data, we obtain high-quality representations of sentences. Experimental results on a broad range of 10 transfer tasks demonstrate that our model outperforms the state-of-the-art unsupervised single methods, including the classical skip-thoughts and the advanced skip-thoughts+LN model. Furthermore, compared with the traditional recurrent neural network, our mean-max AAE greatly reduce the training time.

Connected through hyperedge HE-1 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-2 (induced by source paper "searching for legal clauses by analogy few shot semantic retrieval shared task"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "efficient sentence embedding using discrete cosine transform"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "no training required exploring random encoders for sentence classification"
    - "vector of locally aggregated word embeddings vlawe a novel document level representation"
    - "moverscore text generation evaluating with contextualized embeddings and earth mover distance"
    - "cbow is not all you need combining cbow with the compositional matrix space model"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "mixed pooling multi view attention autoencoder for representation learning in healthcare"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention is all you need"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "semi supervised classification with graph convolutional networks"
    - "gram graph based attention model for healthcare representation learning"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "the effect of downstream classification tasks for evaluating sentence embeddings"):
  with members:
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a large annotated corpus for learning natural language inference"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)