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: "benchmarking classic and learned navigation in complex 3d environments"
  Abstract: Navigation research is attracting renewed interest with the advent of learning-based methods. However, this new line of work is largely disconnected from well-established classic navigation approaches. In this paper, we take a step towards coordinating these two directions of research. We set up classic and learning-based navigation systems in common simulated environments and thoroughly evaluate them in indoor spaces of varying complexity, with access to different sensory modalities. Additionally, we measure human performance in the same environments. We find that a classic pipeline, when properly tuned, can perform very well in complex cluttered environments. On the other hand, learned systems can operate more robustly with a limited sensor suite. Overall, both approaches are still far from human-level performance.

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"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"
    - "sidekick policy learning for active visual exploration"

Connected through hyperedge HE-2 (induced by source paper "target driven visual navigation exploiting object relationships"):
  with members:
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "deduce diverse scene detection methods in unseen challenging environments"
    - "looking at the right stuff guided semantic gaze for autonomous driving"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "on evaluation of embodied navigation agents"
    - "subgoal discovery for hierarchical dialogue policy learning"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "visual semantic navigation using scene priors"

Connected through hyperedge HE-3 (induced by source paper "sparse graphical memory for robust planning"):
  with members:
    - "learning world graphs to accelerate hierarchical reinforcement learning"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "mapping state space using landmarks for universal goal reaching"
    - "hallucinative topological memory for zero shot visual planning"
    - "temporal difference models model free deep rl for model based control"
    - "visual reinforcement learning with imagined goals"
    - "distributed distributional deterministic policy gradients"
    - "hindsight experience replay"

Connected through hyperedge HE-4 (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"
    - "co separating sounds of visual objects"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"
    - "bayesian relational memory for semantic visual navigation"

Connected through hyperedge HE-5 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"
    - "zero shot visual imitation"

Connected through hyperedge HE-6 (induced by source paper "videonavqa bridging the gap between visual and embodied question answering"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "multi target embodied question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "habitat a platform for embodied ai research"
    - "murel multimodal relational reasoning for visual question answering"
    - "learning visual question answering by bootstrapping hard attention"
    - "systematic generalization what is required and can it be learned"
    - "blindfold baselines for embodied qa"

Connected through hyperedge HE-7 (induced by source paper "habitat a platform for embodied ai research"):
  with members:
    - "the replica dataset a digital replica of indoor spaces"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "learning agile and dynamic motor skills for legged robots"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "virtualhome simulating household activities via programs"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-8 (induced by source paper "decentralized distributed ppo solving pointgoal navigation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "on evaluation of embodied navigation agents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)