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: "egocentric spatial memory"
  Abstract: Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspective. We introduce an integrated deep neural network architecture for modeling ESM. It learns to estimate the occupancy state of the world and progressively construct top-down 2D global maps from egocentric views in a spatially extended environment. During the exploration, our proposed ESM model updates belief of the global map based on local observations using a recurrent neural network. It also augments the local mapping with a novel external memory to encode and store latent representations of the visited places over long-term exploration in large environments which enables agents to perform place recognition and hence, loop closure. Our proposed ESM network contributes in the following aspects: (1) without feature engineering, our model predicts free space based on egocentric views efficiently in an end-to-end manner; (2) different from other deep learning-based mapping system, ESMN deals with continuous actions and states which is vitally important for robotic control in real applications. In the experiments, we demonstrate its accurate and robust global mapping capacities in 3D virtual mazes and realistic indoor environments by comparing with several competitive baselines.

Connected through hyperedge HE-1 (induced by source paper "benchmarking classic and learned navigation in complex 3d environments"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "group normalization"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-2 (induced by source paper "discoman dataset of indoor scenes for odometry mapping and navigation"):
  with members:
    - "scene motion decomposition for learnable visual odometry"
    - "panoptic segmentation"
    - "open3d a modern library for 3d data processing"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "slanted stixels representing san francisco s steepest streets"

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.CV(Computer Vision and Pattern Recognition)