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 "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.

Includes members:
  Member "rerere remote embodied referring expressions in real indoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language")
  Member "stay on the path instruction fidelity in vision and language navigation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation")
  Member "the streetlearn environment and dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "touchdown natural language navigation and spatial reasoning in visual street environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "on evaluation of embodied navigation agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an exploration of embodied visual exploration")
  Member "mapping instructions to actions in 3d environments with visual goal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view")
  Member "mapping navigation instructions to continuous control actions with position visitation prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reverie remote embodied visual referring expression in real indoor environments")
  Member "vision and language navigation interpreting visually grounded navigation instructions in real environments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards efficient model compression via learned global ranking")

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)