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: "scalable attentive sentence pair modeling via distilled sentence embedding"
  Abstract: Attention based models have become the new state-of-the-art in natural language understanding tasks such as question-answering and sentence similarity. Recent models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sentence and a set of candidate sentences, requires the propagation of all query-candidate sentence-pairs throughout a stack of cross-attention layers. This exhaustive process becomes computationally prohibitive when the number of candidate sentences is large. In contrast, sentence embedding techniques learn a sentence-to-vector mapping and compute the similarity between the sentence vectors via simple elementary operations such as dot product or cosine similarity. In this paper, we introduce a sentence embedding method that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks. The outline of the proposed method is as follows: Given a cross-attentive teacher model (e.g. a fine-tuned BERT), we train a sentence embedding based student model to reconstruct the sentence-pair scores obtained by the teacher model. We empirically demonstrate the effectiveness of our distillation method on five GLUE sentence-pair tasks. Our method significantly outperforms several ELMO variants and other sentence embedding methods, while accelerating computation of the query-candidate sentence-pairs similarities by several orders of magnitude, with an average relative degradation of 4.6% compared to BERT.

Connected through hyperedge HE-1 (induced by source paper "attentive item2vec neural attentive user representations"):
  with members:
    - "neural attentive multiview machines"
    - "cb2cf a neural multiview content to collaborative filtering model for completely cold item recommendations"
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "neural attentive session based recommendation"

Connected through hyperedge HE-2 (induced by source paper "neural attentive multiview machines"):
  with members:
    - "attentive item2vec neural attentive user representations"
    - "multiscale self attentive convolutions for vision and language modeling"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multimodal machine learning a survey and taxonomy"
    - "gaussian process regression for out of sample extension"
    - "bayesian neural word embedding"
    - "session based recommendations with 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.LG(Machine Learning)