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 "run through the streets a new dataset and baseline models for realistic urban navigation"):
  Abstract: Following navigation instructions in natural language requires a composition of language, action, and knowledge of the environment. Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map. Here we introduce the Realistic Urban Navigation (RUN) task, aimed at interpreting navigation instructions based on a real, dense, urban map. Using Amazon Mechanical Turk, we collected a dataset of 2515 instructions aligned with actual routes over three regions of Manhattan. We propose a strong baseline for the task and empirically investigate which aspects of the neural architecture are important for the RUN success. Our results empirically show that entity abstraction, attention over words and worlds, and a constantly updating world-state, significantly contribute to task accuracy.

Includes members:
  Member "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "learning to map context dependent sentences to executable formal queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how far are we from effective context modeling an exploratory study on semantic parsing in context")
  Member "talk the walk navigating new york city through grounded dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  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 "chalet cornell house agent learning environment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to follow directions in street view")
  Member "vision based navigation with language based assistance via imitation learning with indirect intervention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "environment agnostic multitask learning for natural language grounded navigation")
  Member "mapping instructions to actions in 3d environments with visual goal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view")
  Member "following high level navigation instructions on a simulated quadcopter with imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "affect based intrinsic rewards for exploration and 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.CL(Computation and Language)