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: "on transfer learning using a mac model variant"
  Abstract: We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with a simplified set of equations that achieves comparable accuracy, while training faster. We evaluate both models on CLEVR and CoGenT, and show that, transfer learning with fine-tuning results in a 15 point increase in accuracy, matching the state of the art. Finally, in contrast, we demonstrate that improper fine-tuning can actually reduce a model's accuracy as well.

Connected through hyperedge HE-1 (induced by source paper "remind your neural network to prevent catastrophic forgetting"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "large scale incremental learning"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "answer them all toward universal visual question answering models"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "bilinear attention networks"

Connected through hyperedge HE-2 (induced by source paper "transfer learning in visual and relational reasoning"):
  with members:
    - "leveraging medical visual question answering with supporting facts"
    - "learning by abstraction the neural state machine"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "huggingface s transformers state of the art natural language processing"
    - "do better imagenet models transfer better"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "taskonomy disentangling task transfer learning"
    - "object based reasoning in vqa"

Connected through hyperedge HE-3 (induced by source paper "leveraging medical visual question answering with supporting facts"):
  with members:
    - "towards vqa models that can read"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "object based reasoning in vqa"
    - "compositional attention networks for machine reasoning"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "hadamard product for low rank bilinear pooling"

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.CV(Computer Vision and Pattern Recognition)