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: "grasp2vec learning object representations from self supervised grasping"
  Abstract: Well structured visual representations can make robot learning faster and can improve generalization. In this paper, we study how we can acquire effective object-centric representations for robotic manipulation tasks without human labeling by using autonomous robot interaction with the environment. Such representation learning methods can benefit from continuous refinement of the representation as the robot collects more experience, allowing them to scale effectively without human intervention. Our representation learning approach is based on object persistence: when a robot removes an object from a scene, the representation of that scene should change according to the features of the object that was removed. We formulate an arithmetic relationship between feature vectors from this observation, and use it to learn a representation of scenes and objects that can then be used to identify object instances, localize them in the scene, and perform goal-directed grasping tasks where the robot must retrieve commanded objects from a bin. The same grasping procedure can also be used to automatically collect training data for our method, by recording images of scenes, grasping and removing an object, and recording the outcome. Our experiments demonstrate that this self-supervised approach for tasked grasping substantially outperforms direct reinforcement learning from images and prior representation learning methods.

Connected through hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"

Connected through hyperedge HE-2 (induced by source paper "self supervised graph representation learning via global context prediction"):
  with members:
    - "self supervised dialogue learning"
    - "gmnn graph markov neural networks"
    - "multi stage self supervised learning for graph convolutional networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "attributed graph clustering via adaptive graph convolution"
    - "graph wavelet neural network"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "self supervised point set local descriptors for point cloud registration"):
  with members:
    - "non iterative one step solution for point set registration problem on pose estimation without correspondence"
    - "learning autonomous exploration and mapping with semantic vision"
    - "contextual imagined goals for self supervised robotic learning"
    - "mortonnet self supervised learning of local features in 3d point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"

Connected through hyperedge HE-4 (induced by source paper "visual task progress estimation with appearance invariant embeddings for robot control and planning"):
  with members:
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "self supervised correspondence in visuomotor policy learning"
    - "learning representations by maximizing mutual information across views"
    - "learning actionable representations from visual observations"
    - "playing hard exploration games by watching youtube"
    - "visual reinforcement learning with imagined goals"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-5 (induced by source paper "joint encoding of appearance and motion features with self supervision for first person action recognition"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "unsupervised representation learning by predicting image rotations"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "lsta long short term attention for egocentric action recognition"
    - "audio visual scene analysis with self supervised multisensory features"
    - "representation learning by learning to count"
    - "online action detection"
    - "learning features by watching objects move"

Connected through hyperedge HE-6 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network"):
  with members:
    - "online self supervised learning for object picking detecting optimum grasping position using a metric learning approach"
    - "control barrier functions theory and applications"
    - "residual reinforcement learning for robot control"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "motion switching with sensory and instruction signals by designing dynamical systems using deep neural network"
    - "machine teaching a new paradigm for building machine learning systems"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-7 (induced by source paper "context aware recommendations for televisions using deep embeddings with relaxed n pairs loss objective"):
  with members:
    - "deep joint embeddings of context and content for recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "the importance of context when recommending tv content dataset and algorithms"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "meta prod2vec product embeddings using side information for recommendation"

Connected through hyperedge HE-8 (induced by source paper "a multi task learning framework for grasping position detection and few shot classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning to compare relation network for few shot learning"
    - "learning a rotation invariant detector with rotatable bounding box"
    - "one shot learning with memory augmented neural networks"
    - "ssd single shot multibox detector"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

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.RO(Robotics)