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: "learning to reason with adaptive computation"
  Abstract: Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that learning the correct number of inference steps is difficult. We introduce the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model.

Connected through hyperedge HE-1 (induced by source paper "layer flexible adaptive computational time for recurrent neural networks"):
  with members:
    - "universal transformers"
    - "depth adaptive computational policies for efficient visual tracking"
    - "dynamic prediction length for time series with sequence to sequence networks"
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "early improving recurrent elastic highway network"
    - "learning to skim text"
    - "dynamic computational time for visual attention"
    - "convolutional neural fabrics"

Connected through hyperedge HE-2 (induced by source paper "constructing datasets for multi hop reading comprehension across documents"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "neural question answering at bioasq 5b"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "end to end differentiable proving"
    - "zero shot relation extraction via reading comprehension"
    - "making neural qa as simple as possible but not simpler"

Connected through hyperedge HE-3 (induced by source paper "dynamic computational time for visual attention"):
  with members:
    - "low rank bilinear pooling for fine grained classification"
    - "stacked hourglass networks for human pose estimation"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "adaptive computation time for recurrent neural networks"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "spatially adaptive computation time for residual networks"
    - "mining discriminative triplets of patches for fine grained classification"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

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)