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: "adapting deep visuomotor representations with weak pairwise constraints"
  Abstract: Real-world robotics problems often occur in domains that differ significantly from the robot's prior training environment. For many robotic control tasks, real world experience is expensive to obtain, but data is easy to collect in either an instrumented environment or in simulation. We propose a novel domain adaptation approach for robot perception that adapts visual representations learned on a large easy-to-obtain source dataset (e.g. synthetic images) to a target real-world domain, without requiring expensive manual data annotation of real world data before policy search. Supervised domain adaptation methods minimize cross-domain differences using pairs of aligned images that contain the same object or scene in both the source and target domains, thus learning a domain-invariant representation. However, they require manual alignment of such image pairs. Fully unsupervised adaptation methods rely on minimizing the discrepancy between the feature distributions across domains. We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains. Focusing on adapting from simulation to real world data using a PR2 robot, we evaluate our approach on a manipulation task and show that by using weakly paired images, our method compensates for domain shift more effectively than previous techniques, enabling better robot performance in the real world.

Connected through hyperedge HE-1 (induced by source paper "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "towards segmenting everything that moves"
    - "large scale interactive object segmentation with human annotators"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "shapemask learning to segment novel objects by refining shape priors"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "group normalization"

Connected through hyperedge HE-2 (induced by source paper "adversarial feature training for generalizable robotic visuomotor control"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "affordance learning for end to end visuomotor robot control"
    - "beyond sharing weights for deep domain adaptation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep reinforcement learning to acquire navigation skills for wheel legged robots in complex environments"
    - "deep object centric representations for generalizable robot learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"

Connected through hyperedge HE-3 (induced by source paper "generalization in transfer learning"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "benchmark environments for multitask learning in continuous domains"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-4 (induced by source paper "causal induction from visual observations for goal directed tasks"):
  with members:
    - "gradient based neural dag learning"
    - "environment probing interaction policies"
    - "dag gnn dag structure learning with graph neural networks"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "causal reasoning from meta reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning plannable representations with causal infogan"
    - "woulda coulda shoulda counterfactually guided policy search"

Connected through hyperedge HE-5 (induced by source paper "few shot meta denoising"):
  with members:
    - "adversarial signal denoising with encoder decoder networks"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "unprocessing images for learned raw denoising"
    - "prototypical networks for few shot learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a wavenet for speech denoising"
    - "xunit learning a spatial activation function for efficient image restoration"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-6 (induced by source paper "an introduction to deep reinforcement learning"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "learning to play with intrinsically motivated self aware agents"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "a study on overfitting in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "the limits and potentials of deep learning for robotics"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "task embedded control networks for few shot imitation learning"

Connected through hyperedge HE-8 (induced by source paper "craves controlling robotic arm with a vision based economic system"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "the costar block stacking dataset learning with workspace constraints"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

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)