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: "audio visual embodied navigation"
  Abstract: Moving around in the world is naturally a multisensory experience, but today's embodied agents are deaf - restricted to solely their visual perception of the environment. We introduce audio-visual navigation for complex, acoustically and visually realistic 3D environments. By both seeing and hearing, the agent must learn to navigate to an audio-based target. We develop a multi-modal deep reinforcement learning pipeline to train navigation policies end-to-end from a stream of egocentric audio-visual observations, allowing the agent to (1) discover elements of the geometry of the physical space indicated by the reverberating audio and (2) detect and follow sound-emitting targets. We further introduce audio renderings based on geometrical acoustic simulations for a set of publicly available 3D assets and instrument AI-Habitat to support the new sensor, making it possible to insert arbitrary sound sources in an array of apartment, office, and hotel environments. Our results show that audio greatly benefits embodied visual navigation in 3D spaces.

Connected through hyperedge HE-1 (induced by source paper "look listen and act towards audio visual embodied navigation"):
  with members:
    - "self supervised moving vehicle tracking with stereo sound"
    - "the sound of motions"
    - "self supervised audio visual co segmentation"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"

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)