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: "mental rotation by optimizing transforming distance"
  Abstract: The human visual system is able to recognize objects despite transformations that can drastically alter their appearance. To this end, much effort has been devoted to the invariance properties of recognition systems. Invariance can be engineered (e.g. convolutional nets), or learned from data explicitly (e.g. temporal coherence) or implicitly (e.g. by data augmentation). One idea that has not, to date, been explored is the integration of latent variables which permit a search over a learned space of transformations. Motivated by evidence that people mentally simulate transformations in space while comparing examples, so-called "mental rotation", we propose a transforming distance. Here, a trained relational model actively transforms pairs of examples so that they are maximally similar in some feature space yet respect the learned transformational constraints. We apply our method to nearest-neighbour problems on the Toronto Face Database and NORB.

Connected through hyperedge HE-1 (induced by source paper "shapecodes self supervised feature learning by lifting views to viewgrids"):
  with members:
    - "learning pose specific representations by predicting different views"
    - "deep view morphing"
    - "transitive invariance for self supervised visual representation learning"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "colorization as a proxy task for visual understanding"
    - "learning to look around intelligently exploring unseen environments for unknown tasks"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-2 (induced by source paper "look ahead before you leap end to end active recognition by forecasting the effect of motion"):
  with members:
    - "deep convolutional inverse graphics network"
    - "show attend and tell neural image caption generation with visual attention"
    - "dense optical flow prediction from a static image"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "learning image representations tied to ego motion"
    - "deepstereo learning to predict new views from the world s imagery"
    - "learning to see by moving"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised disentangling with recurrent transformations for 3d view synthesis"):
  with members:
    - "deep convolutional inverse graphics network"
    - "action conditional video prediction using deep networks in atari games"
    - "understanding deep features with computer generated imagery"
    - "fast r cnn"
    - "deepstereo learning to predict new views from the world s imagery"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "discovering hidden factors of variation in deep networks"

Connected through hyperedge HE-4 (induced by source paper "compensating for large in plane rotations in natural images"):
  with members:
    - "spatial transformer networks"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-5 (induced by source paper "gated networks an inventory"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "continuous control with deep reinforcement learning"
    - "modeling sequential data using higher order relational features and predictive training"
    - "generative class conditional autoencoders"
    - "deep learning of representations looking forward"
    - "on multi view feature learning"

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.LG(Machine Learning)