human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "visual task progress estimation with appearance invariant embeddings for robot control and planning"):
  Abstract: To fulfill the vision of full autonomy, robots must be capable of reasoning about the state of the world. In vision-based tasks, this means that a robot must understand the dissimilarities between its current perception of the environment with that of another state. To be of practical use, this dissimilarity must be quantifiable and computed over scenes with different viewpoints, nature (simulated vs. real), and appearances (shape, color, luminosity, etc.). Motivated by this problem, we propose an approach that uses the consistency of the progress among different examples and viewpoints of a task to train a deep neural network to map images into measurable features. Our method builds upon Time-Contrastive Networks (TCNs), originally proposed as a representation for continuous visuomotor skill learning, to train the network using only discrete snapshots taken at different stages of a task such that the network becomes sensitive to differences in task phases. We associate these embeddings to a sequence of images representing gradual task accomplishment, allowing a robot to iteratively query its motion planner with the current visual state to solve long-horizon tasks. We quantify the granularity achieved by the network in recognizing the number of objects in a scene and in measuring the volume of liquid in a cup. Our experiments leverage this granularity to make a mobile robot move a desired number of objects into a storage area and to control the amount of pouring in a cup.

Includes members:
  Member "self supervised sim to real adaptation for visual robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "self supervised correspondence in visuomotor policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a geometric perspective on visual imitation learning")
  Member "learning representations by maximizing mutual information across views":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "grasp2vec learning object representations from self supervised grasping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "learning actionable representations from visual observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active perception and representation for robotic manipulation")
  Member "playing hard exploration games by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "visual reinforcement learning with imagined goals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "one shot imitation from observing humans via domain adaptive meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting meta learning as supervised learning")

Question: Please tell me which class the source paper 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)