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: "synthesizing manipulation sequences for under specified tasks using unrolled markov random fields"
  Abstract: Many tasks in human environments require per- forming a sequence of navigation and manipulation steps involving objects. In unstructured human environments, the location and configuration of the objects involved often change in unpredictable ways. This requires a high-level planning strategy that is robust and flexible in an uncertain environment. We propose a novel dynamic planning strategy, which can be trained from a set of example sequences. High level tasks are expressed as a sequence of primitive actions or controllers (with appropriate parameters). Our score function, based on Markov Random Field (MRF), captures the relations between environ- ment, controllers, and their arguments. By expressing the envi- ronment using sets of attributes, the approach generalizes well to unseen scenarios. We train the parameters of our MRF using a maximum margin learning method. We provide a detailed empirical validation of our overall framework demonstrating successful plan strategies for a variety of tasks. 1

Connected through hyperedge HE-1 (induced by source paper "neural task planning with and or graph representations"):
  with members:
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "learning model based planning from scratch"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "diversified visual attention networks for fine grained object classification"
    - "visual semantic planning using deep successor representations"
    - "cognitive mapping and planning for visual navigation"

Connected through hyperedge HE-2 (induced by source paper "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"):
  with members:
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "learning articulated motions from visual demonstration"
    - "deep learning for detecting robotic grasps"
    - "representation learning a review and new perspectives"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "unstructured human activity detection from rgbd images"
    - "a probabilistic framework for learning kinematic models of articulated objects"

Connected through hyperedge HE-3 (induced by source paper "deep multimodal embedding manipulating novel objects with point clouds language and trajectories"):
  with members:
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "learning contact rich manipulation skills with guided policy search"
    - "deep learning for detecting robotic grasps"
    - "exploiting similarities among languages for machine translation"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"
    - "difference of normals as a multi scale operator in unorganized point clouds"

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)