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: "mechanical search multi step retrieval of a target object occluded by clutter"
  Abstract: When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects from cluttered bins, shelves, or tables. Mechanical Search describes the class of tasks where the goal is to locate and extract a known target object. In this paper, we formalize Mechanical Search and study a version where distractor objects are heaped over the target object in a bin. The robot uses an RGBD perception system and control policies to iteratively select, parameterize, and perform one of 3 actions -- push, suction, grasp -- until the target object is extracted, or either a time limit is exceeded, or no high confidence push or grasp is available. We present a study of 5 algorithmic policies for mechanical search, with 15,000 simulated trials and 300 physical trials for heaps ranging from 10 to 20 objects. Results suggest that success can be achieved in this long-horizon task with algorithmic policies in over 95% of instances and that the number of actions required scales approximately linearly with the size of the heap. Code and supplementary material can be found at this http URL .

Connected through hyperedge HE-1 (induced by source paper "toward sim to real directional semantic grasping"):
  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"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "mqa answering the question via robotic manipulation"):
  with members:
    - "improving generative visual dialog by answering diverse questions"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "neural modular control for embodied question answering"
    - "interactive perception leveraging action in perception and perception in action"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"

Connected through hyperedge HE-3 (induced by source paper "a deep learning approach to grasping the invisible"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "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"
    - "hindsight experience replay"

Connected through hyperedge HE-4 (induced by source paper "6 dof grasping for target driven object manipulation in clutter"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "grasping unknown objects in clutter by superquadric representation"

Connected through hyperedge HE-5 (induced by source paper "dynamics learning with cascaded variational inference for multi step manipulation"):
  with members:
    - "learning action representations for reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-6 (induced by source paper "object finding in cluttered scenes using interactive perception"):
  with members:
    - "learning 6 dof grasping and pick place using attention focus"
    - "open3d a modern library for 3d data processing"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "interactive perception leveraging action in perception and perception in action"
    - "proximal policy optimization algorithms"
    - "revisiting active perception"

Connected through hyperedge HE-7 (induced by source paper "split deep q learning for robust object singulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "mask r cnn"
    - "continuous control with deep reinforcement learning"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "data driven grasp synthesis a survey"
    - "deep learning for detecting robotic grasps"

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)