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: "energy based self attentive learning of abstractive communities for spoken language understanding"
  Abstract: Abstractive Community Detection is an important Spoken Language Understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then evaluate it against multiple baselines within the powerful siamese and triplet energy-based meta-architectures. Moreover, we propose a general sampling scheme that enables the triplet architecture to capture subtle clustering patterns, such as overlapping and nested communities. Experiments on the AMI corpus show that our system improves on the state-of-the-art and that our triplet sampling scheme is effective. Code and data are publicly available.

Connected through hyperedge HE-1 (induced by source paper "message passing attention networks for document understanding"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph convolutional networks for text classification"
    - "how powerful are graph neural networks"
    - "kernel graph convolutional neural networks"
    - "neural message passing for quantum chemistry"
    - "a structured self attentive sentence embedding"
    - "graph classification with 2d convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "automatically learning construction injury precursors from text"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a structured self attentive sentence embedding"
    - "enriching word vectors with subword information"
    - "word embeddings for the construction domain"
    - "character level convolutional networks for text classification"
    - "visualizing and understanding neural models in nlp"

Connected through hyperedge HE-3 (induced by source paper "bidirectional context aware hierarchical attention network for document understanding"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "context aware self attention networks"
    - "deep communicating agents for abstractive summarization"
    - "improving the transformer translation model with document level context"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "context aware neural machine translation learns anaphora resolution"
    - "three factors influencing minima in sgd"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "linto assistant vocal open source respectueux des donn ees personnelles pour les r eunions d entreprise"):
  with members:
    - "unsupervised abstractive meeting summarization with multi sentence compression and budgeted submodular maximization"
    - "snorkel rapid training data creation with weak supervision"
    - "neural machine translation by jointly learning to align and translate"

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)