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: "the bottom up evolution of representations in the transformer a study with machine translation and language modeling objectives"
  Abstract: We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We focus on the Transformers for our analysis as they have been shown effective on various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and how this process depends on the choice of learning objective. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers.

Connected through hyperedge HE-1 (induced by source paper "fixed encoder self attention patterns in transformer based machine translation"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "transfusion understanding transfer learning for medical imaging"):
  with members:
    - "similarity of neural network representations revisited"
    - "investigating multilingual nmt representations at scale"
    - "rethinking imagenet pre training"
    - "direct uncertainty prediction for medical second opinions"
    - "do better imagenet models transfer better"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "taskonomy disentangling task transfer learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

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)