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: "multi task domain adaptation for deep learning of instance grasping from simulation"
  Abstract: Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain-adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real-world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.

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 "learning fast adaptation with meta strategy optimization"):
  with members:
    - "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "sim to real transfer for biped locomotion"
    - "norml no reward meta learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"

Connected through hyperedge HE-3 (induced by source paper "anchor tasks inexpensive shared and aligned tasks for domain adaptation"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "regularizing deep networks by modeling and predicting label structure"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "keto learning keypoint representations for tool manipulation"):
  with members:
    - "kpam keypoint affordances for category level robotic manipulation"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "robot bed making deep transfer learning using depth sensing of deformable fabric"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "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"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-5 (induced by source paper "directional semantic grasping of real world objects from simulation to reality"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "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-6 (induced by source paper "a deep learning approach to grasping the invisible"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "light weight refinenet for real time semantic segmentation"
    - "robust 6d object pose estimation in cluttered scenes using semantic segmentation and pose regression networks"

Connected through hyperedge HE-7 (induced by source paper "grasp2vec learning object representations from self supervised grasping"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "self supervised visual planning with temporal skip connections"
    - "hindsight experience replay"
    - "grasp pose detection in point clouds"
    - "the intentional unintentional agent learning to solve many continuous control tasks simultaneously"
    - "deep predictive policy training using reinforcement learning"
    - "end to end learning of semantic grasping"

Connected through hyperedge HE-8 (induced by source paper "behavioural repertoire via generative adversarial policy networks"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "diversity is all you need learning skills without a reward function"
    - "semi supervised haptic material recognition for robots using generative adversarial networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "improved training of wasserstein gans"
    - "quality and diversity optimization a unifying modular framework"
    - "photo realistic single image super resolution using a generative adversarial network"

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.LG(Machine Learning)