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: "shapecodes self supervised feature learning by lifting views to viewgrids"
  Abstract: We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the object to be predictable from learned features. We implement this idea as an encoder-decoder convolutional neural network. The network maps an input image of an unknown category and unknown viewpoint to a latent space, from which a deconvolutional decoder can best "lift" the image to its complete viewgrid showing the object from all viewing angles. Our class-agnostic training procedure encourages the representation to capture fundamental shape primitives and semantic regularities in a data-driven manner---without manual semantic labels. Our results on two widely-used shape datasets show 1) our approach successfully learns to perform "mental rotation" even for objects unseen during training, and 2) the learned latent space is a powerful representation for object recognition, outperforming several existing unsupervised feature learning methods.

Connected through hyperedge HE-1 (induced by source paper "emergence of exploratory look around behaviors through active observation completion"):
  with members:
    - "learning exploration policies for navigation"
    - "learning real world robot policies by dreaming"
    - "sidekick policy learning for active visual exploration"
    - "semi parametric topological memory for navigation"
    - "world models"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-2 (induced by source paper "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"):
  with members:
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "videobert a joint model for video and language representation learning"
    - "cross lingual language model pretraining"
    - "nocaps novel object captioning at scale"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "extreme relative pose network under hybrid representations"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "recurrent transformer networks for semantic correspondence"
    - "fast and flexible indoor scene synthesis via deep convolutional generative models"
    - "extreme relative pose estimation for rgb d scans via scene completion"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "unsupervised learning of depth and ego motion from video"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "relative camera pose estimation using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "extreme relative pose estimation for rgb d scans via scene completion"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "open3d a modern library for 3d data processing"
    - "recurrent transformer networks for semantic correspondence"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "unsupervised learning of depth and ego motion from video"
    - "relative camera pose estimation using convolutional neural networks"
    - "matterport3d learning from rgb d data in indoor environments"

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)