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: "effective and general evaluation for instruction conditioned navigation using dynamic time warping"
  Abstract: In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate through an environment. Datasets for studying this task typically contain pairs of these instructions and reference trajectories. Yet, most evaluation metrics used thus far fail to properly account for the latter, relying instead on insufficient similarity comparisons. We address fundamental flaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents. For such, we define the normalized Dynamic Time Warping (nDTW) metric, that softly penalizes deviations from the reference path, is naturally sensitive to the order of the nodes composing each path, is suited for both continuous and graph-based evaluations, and can be efficiently calculated. Further, we define SDTW, which constrains nDTW to only successful paths. We collect human similarity judgments for simulated paths and find nDTW correlates better with human rankings than all other metrics. We also demonstrate that using nDTW as a reward signal for Reinforcement Learning navigation agents improves their performance on both the Room-to-Room (R2R) and Room-for-Room (R4R) datasets. The R4R results in particular highlight the superiority of SDTW over previous success-constrained metrics.

Connected through hyperedge HE-1 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view"):
  with members:
    - "navigation agents for the visually impaired a sidewalk simulator and experiments"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "valan vision and language agent navigation"
    - "the streetlearn environment and dataset"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-2 (induced by source paper "perceive transform and act multi modal attention networks for vision and language navigation"):
  with members:
    - "embodied vision and language navigation with dynamic convolutional filters"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "embodied visual recognition"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "situational fusion of visual representation for visual navigation"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-3 (induced by source paper "valan vision and language agent navigation"):
  with members:
    - "vision and dialog navigation"
    - "seed rl scalable and efficient deep rl with accelerated central inference"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "transferable representation learning in vision and language navigation"
    - "dopamine a research framework for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

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)