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: "depth adaptive transformer"
  Abstract: State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence. Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity. On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.

Connected through hyperedge HE-1 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"
    - "a modern take on the bias variance tradeoff in neural networks"

Connected through hyperedge HE-2 (induced by source paper "depth adaptive graph recurrent network for text classification"):
  with members:
    - "graph based neural sentence ordering"
    - "r transformer recurrent neural network enhanced transformer"
    - "learning to remember more with less memorization"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "cm net a novel collaborative memory network for spoken language understanding"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "graph to sequence learning using gated graph 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)