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: "unsupervised learning of object keypoints for perception and control"
  Abstract: The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates. Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods. Furthermore, consistent long-term tracking enables two notable results in control domains -- (1) using the keypoint co-ordinates and corresponding image features as inputs enables highly sample-efficient reinforcement learning; (2) learning to explore by controlling keypoint locations drastically reduces the search space, enabling deep exploration (leading to states unreachable through random action exploration) without any extrinsic rewards.

Connected through hyperedge HE-1 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of object structure and dynamics from videos"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "unsupervised discovery of parts structure and dynamics"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "mt vae learning motion transformations to generate multimodal human dynamics"

Connected through hyperedge HE-3 (induced by source paper "self supervised correspondence in visuomotor policy learning"):
  with members:
    - "kpam keypoint affordances for category level robotic manipulation"
    - "graph structured visual imitation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised visuomotor control through distributional planning networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"

Connected through hyperedge HE-4 (induced by source paper "action conditioned benchmarking of robotic video prediction models a comparative study"):
  with members:
    - "improved conditional vrnns for video prediction"
    - "videoflow a flow based generative model for video"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "joint learning of unsupervised object based perception and control"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "multi object representation learning with iterative variational inference"
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "strategic object oriented reinforcement learning"
    - "relational deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "no representation without transformation"):
  with members:
    - "learning to discover novel visual categories via deep transfer clustering"
    - "dynamics aware unsupervised discovery of skills"
    - "temporal cycle consistency learning"
    - "shaping belief states with generative environment models for rl"
    - "neural processes"
    - "neural predictive belief representations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "towards a definition of disentangled representations"

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)