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: "robot learning in homes improving generalization and reducing dataset bias"
  Abstract: Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in curated lab settings. If we aim to deploy these models in unstructured visual environments like people's homes, they will be unable to cope with the mismatch in data distribution. In such light, we present the first systematic effort in collecting a large dataset for robotic grasping in homes. First, to scale and parallelize data collection, we built a low cost mobile manipulator assembled for under 3K USD. Second, data collected using low cost robots suffer from noisy labels due to imperfect execution and calibration errors. To handle this, we develop a framework which factors out the noise as a latent variable. Our model is trained on 28K grasps collected in several houses under an array of different environmental conditions. We evaluate our models by physically executing grasps on a collection of novel objects in multiple unseen homes. The models trained with our home dataset showed a marked improvement of 43.7% over a baseline model trained with data collected in lab. Our architecture which explicitly models the latent noise in the dataset also performed 10% better than one that did not factor out the noise. We hope this effort inspires the robotics community to look outside the lab and embrace learning based approaches to handle inaccurate cheap robots.

Connected through hyperedge HE-1 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "contrastive multiview coding"
    - "learning robotic manipulation through visual planning and acting"
    - "learning to manipulate deformable objects without demonstrations"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-2 (induced by source paper "tadam a robust stochastic gradient optimizer"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "a robust adaptive stochastic gradient method for deep learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximal policy optimization algorithms"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-4 (induced by source paper "robonet large scale multi robot learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "manipulation by feel touch based control with deep predictive models"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "scaling autoregressive video models"
    - "exploration by random network distillation"

Connected through hyperedge HE-5 (induced by source paper "meta world a benchmark and evaluation for multi task and meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta learning by the baldwin effect"

Connected through hyperedge HE-6 (induced by source paper "rubi reducing unimodal biases in visual question answering"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "right for the right reason training agnostic networks"
    - "explicit bias discovery in visual question answering models"
    - "bilinear attention networks"
    - "from recognition to cognition visual commonsense reasoning"
    - "women also snowboard overcoming bias in captioning models"

Connected through hyperedge HE-7 (induced by source paper "learning to manipulate deformable objects without demonstrations"):
  with members:
    - "deep imitation learning of sequential fabric smoothing policies"
    - "learning robotic manipulation through visual planning and acting"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "robot bed making deep transfer learning using depth sensing of deformable fabric"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-8 (induced by source paper "grasping in the wild learning 6dof closed loop grasping from low cost demonstrations"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

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)