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: "inductive visual localisation factorised training for superior generalisation"
  Abstract: End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition. However, RNNs often struggle to generalise to sequences longer than the ones encountered during training. In this work, we propose to optimise neural networks explicitly for induction. The idea is to first decompose the problem in a sequence of inductive steps and then to explicitly train the RNN to reproduce such steps. Generalisation is achieved as the RNN is not allowed to learn an arbitrary internal state; instead, it is tasked with mimicking the evolution of a valid state. In particular, the state is restricted to a spatial memory map that tracks parts of the input image which have been accounted for in previous steps. The RNN is trained for single inductive steps, where it produces updates to the memory in addition to the desired output. We evaluate our method on two different visual recognition problems involving visual sequences: (1) text spotting, i.e. joint localisation and reading of text in images containing multiple lines (or a block) of text, and (2) sequential counting of objects in aerial images. We show that inductive training of recurrent models enhances their generalisation ability on challenging image datasets.

Connected through hyperedge HE-1 (induced by source paper "the ladder algorithm finding repetitive structures in medical images by induction"):
  with members:
    - "deepspine automated lumbar vertebral segmentation disc level designation and spinal stenosis grading using deep learning"
    - "flowing convnets for human pose estimation in videos"
    - "recurrent instance segmentation"
    - "adam a method for stochastic optimization"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "return of the devil in the details delving deep into convolutional nets"
    - "learning human pose estimation features with convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "autocorrect deep inductive alignment of noisy geometric annotations"):
  with members:
    - "smooth loss functions for deep top k classification"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "cycada cycle consistent adversarial domain adaptation"
    - "making neural programming architectures generalize via recursion"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "spatial memory for context reasoning in object detection"
    - "training a feedback loop for hand pose estimation"

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)