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: "jointly learning to label sentences and tokens"
  Abstract: Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size. Methods for directly supervising language composition can allow us to guide the models based on existing knowledge, regularizing them towards more robust and interpretable representations. In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens. The predictions at each level are combined together using an attention mechanism, with token-level labels also acting as explicit supervision for composing sentence-level representations. Our experiments show that by learning to perform these tasks jointly on multiple levels, the model achieves substantial improvements for both sentence classification and sequence labeling.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "select answer and explain interpretable multi hop reading comprehension over multiple documents"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "roberta a robustly optimized bert pretraining approach"
    - "densely connected attention propagation for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "detecting local insights from global labels supervised zero shot sequence labeling via a convolutional decomposition"):
  with members:
    - "context is key grammatical error detection with contextual word representations"
    - "attention is not explanation"
    - "understanding convolutional neural networks for text classification"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised multitask learning for sequence labeling"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "multi head multi layer attention to deep language representations for grammatical error detection"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "adam a method for stochastic optimization"

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)