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: "generic 3d representation via pose estimation and matching"
  Abstract: Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross modality pose estimation).

Connected through hyperedge HE-1 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-2 (induced by source paper "data efficient image recognition with contrastive predictive coding"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "fast autoaugment"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-3 (induced by source paper "attentive context normalization for robust permutation equivariant learning"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "lf net learning local features from images"
    - "group normalization"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "spectral normalization for generative adversarial networks"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "deep sets"

Connected through hyperedge HE-4 (induced by source paper "which tasks should be learned together in multi task learning"):
  with members:
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "zero shot task transfer"
    - "task2vec task embedding for meta learning"
    - "taskonomy disentangling task transfer learning"
    - "multi task learning as multi objective optimization"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "progressive neural networks"

Connected through hyperedge HE-5 (induced by source paper "geometry aware neural rendering"):
  with members:
    - "consistent generative query networks"
    - "learning dexterous in hand manipulation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning models for visual 3d localization with implicit mapping"
    - "unifying map and landmark based representations for visual navigation"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "zero shot task transfer"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "latent ransac"
    - "bayesian model agnostic meta learning"
    - "taskonomy disentangling task transfer learning"
    - "adversarial data programming using gans to relax the bottleneck of curated labeled data"
    - "domain adaptation for visual applications a comprehensive survey"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "metric based regularization and temporal ensemble for multi task learning using heterogeneous unsupervised tasks"):
  with members:
    - "transfer adaptation learning a decade survey"
    - "boosting self supervised learning via knowledge transfer"
    - "improvements to context based self supervised learning"
    - "multi task self supervised visual learning"
    - "a survey on deep learning in medical image analysis"
    - "w net a deep model for fully unsupervised image segmentation"
    - "a survey on multi task learning"
    - "deep retinal image understanding"

Connected through hyperedge HE-8 (induced by source paper "taskonomy disentangling task transfer learning"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "transitive invariance for self supervised visual representation learning"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "multi task self supervised visual learning"
    - "matterport3d learning from rgb d data in indoor environments"
    - "representation learning by learning to count"
    - "playing for benchmarks"

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)