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: "splitnet sim2sim and task2task transfer for embodied visual navigation"
  Abstract: We propose SplitNet, a method for decoupling visual perception and policy learning. By incorporating auxiliary tasks and selective learning of portions of the model, we explicitly decompose the learning objectives for visual navigation into perceiving the world and acting on that perception. We show dramatic improvements over baseline models on transferring between simulators, an encouraging step towards Sim2Real. Additionally, SplitNet generalizes better to unseen environments from the same simulator and transfers faster and more effectively to novel embodied navigation tasks. Further, given only a small sample from a target domain, SplitNet can match the performance of traditional end-to-end pipelines which receive the entire dataset. Code is available this https URL

Connected through hyperedge HE-1 (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"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"
    - "bayesian relational memory for semantic visual navigation"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based visual navigation with information theoretic regularization"):
  with members:
    - "learning a controller fusion network by online trajectory filtering for vision based uav racing"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "reinforcement learning from imperfect demonstrations"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "oil observational imitation learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"

Connected through hyperedge HE-3 (induced by source paper "neonav improving the generalization of visual navigation via generating next expected observations"):
  with members:
    - "habitat a platform for embodied ai research"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning and querying fast generative models for reinforcement learning"
    - "visual semantic navigation using scene priors"
    - "learning to navigate in cities without a map"
    - "gated path planning networks"

Connected through hyperedge HE-4 (induced by source paper "decentralized distributed ppo solving pointgoal navigation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "end to end model free reinforcement learning for urban driving using implicit affordances"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "is deep reinforcement learning really superhuman on atari leveling the playing field"
    - "learning by cheating"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "end to end race driving with deep reinforcement learning"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "end to end vehicle lateral control using a single fisheye camera"

Connected through hyperedge HE-6 (induced by source paper "dd ppo learning near perfect pointgoal navigators from 2 5 billion frames"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "manga method agnostic neural policy generalization and adaptation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "sim to real transfer for biped locomotion"
    - "norml no reward meta learning"
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "exploration by random network distillation"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"

Connected through hyperedge HE-8 (induced by source paper "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "decentralized distributed ppo solving pointgoal navigation"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "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"
    - "gibson env real world perception for embodied 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)