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: "gibson env real world perception for embodied agents"
  Abstract: Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning-in-simulation which consequently casts a question on whether the results transfer to real-world. In this paper, we are concerned with the problem of developing real-world perception for active agents, propose Gibson Virtual Environment for this purpose, and showcase sample perceptual tasks learned therein. Gibson is based on virtualizing real spaces, rather than using artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings. The main characteristics of Gibson are: I. being from the real-world and reflecting its semantic complexity, II. having an internal synthesis mechanism, "Goggles", enabling deploying the trained models in real-world without needing further domain adaptation, III. embodiment of agents and making them subject to constraints of physics and space.

Connected through hyperedge HE-1 (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"
    - "learning exploration policies for navigation"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for active human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end recovery of human shape and pose"
    - "a dataset for developing and benchmarking active vision"
    - "towards accurate multi person pose estimation in the wild"

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"
    - "learning exploration policies for navigation"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "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 "learning view and target invariant visual servoing for navigation"):
  with members:
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "semi parametric topological memory for navigation"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-5 (induced by source paper "topological mapping for manhattan like repetitive environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "deep residual learning for image recognition"
    - "place categorization and semantic mapping on a mobile robot"

Connected through hyperedge HE-6 (induced by source paper "audio visual embodied navigation"):
  with members:
    - "two body problem collaborative visual task completion"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "do autonomous agents benefit from hearing"
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "co separating sounds of visual objects"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"

Connected through hyperedge HE-7 (induced by source paper "scaling and benchmarking self supervised visual representation learning"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "learning to separate object sounds by watching unlabeled video"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-8 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "chalet cornell house agent learning environment"
    - "a critical investigation of deep reinforcement learning for navigation"

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.AI(Artificial Intelligence)