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: "a deep learning based behavioral approach to indoor autonomous navigation"
  Abstract: We present a semantically rich graph representation for indoor robotic navigation. Our graph representation encodes: semantic locations such as offices or corridors as nodes, and navigational behaviors such as enter office or cross a corridor as edges. In particular, our navigational behaviors operate directly from visual inputs to produce motor controls and are implemented with deep learning architectures. This enables the robot to avoid explicit computation of its precise location or the geometry of the environment, and enables navigation at a higher level of semantic abstraction. We evaluate the effectiveness of our representation by simulating navigation tasks in a large number of virtual environments. Our results show that using a simple sets of perceptual and navigational behaviors, the proposed approach can successfully guide the way of the robot as it completes navigational missions such as going to a specific office. Furthermore, our implementation shows to be effective to control the selection and switching of behaviors.

Connected through hyperedge HE-1 (induced by source paper "high level plan for behavioral robot navigation with natural language directions and r net"):
  with members:
    - "translating navigation instructions in natural language to a high level plan for behavioral robot navigation"
    - "neural program meta induction"

Connected through hyperedge HE-2 (induced by source paper "3d scene graph a structure for unified semantics 3d space and camera"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "learning human object interactions by graph parsing neural networks"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "blitznet a real time deep network for scene understanding"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-3 (induced by source paper "a behavioral approach to visual navigation with graph localization networks"):
  with members:
    - "semi parametric topological memory for navigation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "relational inductive biases deep learning and graph networks"
    - "visual semantic navigation using scene priors"
    - "how powerful are graph neural networks"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-4 (induced by source paper "deep local trajectory replanning and control for robot navigation"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "driving policy transfer via modularity and abstraction"
    - "mutan multimodal tucker fusion for visual question answering"
    - "simple online and realtime tracking with a deep association metric"
    - "social attention modeling attention in human crowds"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-5 (induced by source paper "translating navigation instructions in natural language to a high level plan for behavioral robot navigation"):
  with members:
    - "dynamic memory networks for visual and textual question answering"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"

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)