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: "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
  Abstract: There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory. In order to collect a large number of manipulation demonstrations for different objects, we developed a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot can even manipulate objects it has never seen before.

Connected through hyperedge HE-1 (induced by source paper "sub goal trees a framework for goal based reinforcement learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "auto conditioned recurrent mixture density networks for complex trajectory generation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "motion planning networks"
    - "floyd warshall reinforcement learning learning from past experiences to reach new goals"
    - "hindsight experience replay"
    - "prediction and control with temporal segment models"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-3 (induced by source paper "learning robotic manipulation through visual planning and acting"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"
    - "model ensemble trust region policy optimization"
    - "visual reinforcement learning with imagined goals"
    - "learning plannable representations with causal infogan"
    - "spectral normalization for generative adversarial networks"
    - "bias and generalization in deep generative models an empirical study"

Connected through hyperedge HE-4 (induced by source paper "kaolin a pytorch library for accelerating 3d deep learning research"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "meshcnn a network with an edge"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-5 (induced by source paper "toward simulating environments in reinforcement learning based recommendations"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "reinforcement learning for online information seeking"
    - "reinforcement learning based recommender system using biclustering technique"
    - "top k off policy correction for a reinforce recommender system"
    - "deep reinforcement learning based recommendation with explicit user item interactions modeling"

Connected through hyperedge HE-6 (induced by source paper "scaling robot supervision to hundreds of hours with roboturk robotic manipulation dataset through human reasoning and dexterity"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"
    - "know what you don t know unanswerable questions for squad"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "multiple interactions made easy mime large scale demonstrations data for imitation"
    - "baxter s homunculus virtual reality spaces for teleoperation in manufacturing"

Connected through hyperedge HE-7 (induced by source paper "a narration based reward shaping approach using grounded natural language commands"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "grounding natural language commands to starcraft ii game states for narration guided reinforcement learning"
    - "on reinforcement learning for full length game of starcraft"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "reward learning from narrated demonstrations"

Connected through hyperedge HE-8 (induced by source paper "from language to goals inverse reinforcement learning for vision based instruction following"):
  with members:
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "meta inverse reinforcement learning via maximum reward sharing for human motion analysis"
    - "film visual reasoning with a general conditioning layer"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "grounded language learning in a simulated 3d world"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "embodied question answering"

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)