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: "learning instance segmentation by interaction"
  Abstract: We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-supervised manner. The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels. The model learned from over 50K interactions generalizes to novel objects and backgrounds. To deal with noisy training signal for segmenting objects obtained by self-supervised interactions, we propose robust set loss. A dataset of robot's interactions along-with a few human labeled examples is provided as a benchmark for future research. We test the utility of the learned segmentation model by providing results on a downstream vision-based control task of rearranging multiple objects into target configurations from visual inputs alone. Videos, code, and robotic interaction dataset are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "self supervised object in gripper segmentation from robotic motions"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "towards segmenting everything that moves"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "photorealistic image synthesis for object instance detection"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "deep visual domain adaptation a survey"
    - "object discovery in videos as foreground motion clustering"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-3 (induced by source paper "embodied visual recognition"):
  with members:
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gibson env real world perception for embodied agents"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "neural modular control for embodied question answering"
    - "learning to see the invisible end to end trainable amodal instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "self supervised 6d object pose estimation for robot manipulation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-5 (induced by source paper "towards object detection from motion"):
  with members:
    - "online object representations with contrastive learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "pves position velocity encoders for unsupervised learning of structured state representations"
    - "time contrastive networks self supervised learning from video"
    - "weakly supervised semantic segmentation using motion cues"
    - "object centric representation learning from unlabeled videos"
    - "learning features by watching objects move"

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)