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: "learning exploration policies for navigation"
  Abstract: Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much less attention. In this work, we study how agents can autonomously explore realistic and complex 3D environments without the context of task-rewards. We propose a learning-based approach and investigate different policy architectures, reward functions, and training paradigms. We find that the use of policies with spatial memory that are bootstrapped with imitation learning and finally finetuned with coverage rewards derived purely from on-board sensors can be effective at exploring novel environments. We show that our learned exploration policies can explore better than classical approaches based on geometry alone and generic learning-based exploration techniques. Finally, we also show how such task-agnostic exploration can be used for down-stream tasks. Code and Videos are available at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation"):
  with members:
    - "badgr an autonomous self supervised learning based navigation system"
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "beyond photometric loss for self supervised ego motion estimation"
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "self supervised learning of depth and motion under photometric inconsistency"

Connected through hyperedge HE-2 (induced by source paper "an exploration of embodied visual exploration"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "where to look next unsupervised active visual exploration on 360 input"
    - "embodied visual recognition"
    - "the replica dataset a digital replica of indoor spaces"
    - "emergence of exploratory look around behaviors through active observation completion"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "sidekick policy learning for active visual exploration"

Connected through hyperedge HE-3 (induced by source paper "learning to move with affordance maps"):
  with members:
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "driving policy transfer via modularity and abstraction"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"

Connected through hyperedge HE-4 (induced by source paper "affect based intrinsic rewards for exploration and learning"):
  with members:
    - "monocular plan view networks for autonomous driving"
    - "explorations and lessons learned in building an autonomous formula sae car from simulations"
    - "large scale study of curiosity driven learning"
    - "visceral machines risk aversion in reinforcement learning with intrinsic physiological rewards"
    - "on learning intrinsic rewards for policy gradient methods"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "episodic curiosity through reachability"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"

Connected through hyperedge HE-5 (induced by source paper "cross view semantic segmentation for sensing surroundings"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "high resolution representations for labeling pixels and regions"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross view image synthesis using conditional gans"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "visual memory for robust path following"
    - "gibson env real world perception for embodied agents"

Connected through hyperedge HE-6 (induced by source paper "emergence of exploratory look around behaviors through active observation completion"):
  with members:
    - "learning real world robot policies by dreaming"
    - "sidekick policy learning for active visual exploration"
    - "semi parametric topological memory for navigation"
    - "world models"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "deep view morphing"

Connected through hyperedge HE-7 (induced by source paper "simultaneous mapping and target driven navigation"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "habitat a platform for embodied ai research"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-8 (induced by source paper "egomap projective mapping and structured egocentric memory for deep rl"):
  with members:
    - "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"
    - "gibson env real world perception for embodied agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning to navigate in cities without a map"
    - "home a household multimodal environment"
    - "attention is all you need"
    - "neural map structured memory for deep reinforcement learning"
    - "iqa visual question answering in interactive 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)