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: "semi parametric topological memory for navigation"
  Abstract: We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semi-parametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations. The graph stores no metric information, only connectivity of locations corresponding to the nodes. We use SPTM as a planning module in a navigation system. Given only 5 minutes of footage of a previously unseen maze, an SPTM-based navigation agent can build a topological map of the environment and use it to confidently navigate towards goals. The average success rate of the SPTM agent in goal-directed navigation across test environments is higher than the best-performing baseline by a factor of three. A video of the agent is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (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"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"

Connected through hyperedge HE-3 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin"):
  with members:
    - "walking with mind mental imagery enhanced embodied qa"
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "graph attention memory for visual navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi target embodied question answering"
    - "learning your way without map or compass panoramic target driven visual navigation"

Connected through hyperedge HE-4 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement"):
  with members:
    - "gradient surgery for multi task learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "learning to reach goals without reinforcement learning"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning latent plans from play"
    - "advantage weighted regression simple and scalable off policy reinforcement learning"
    - "deepmind control suite"

Connected through hyperedge HE-5 (induced by source paper "learning to move with affordance maps"):
  with members:
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning exploration policies for navigation"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "gibson env real world perception for embodied agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "driving policy transfer via modularity and abstraction"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"

Connected through hyperedge HE-6 (induced by source paper "badgr an autonomous self supervised learning based navigation system"):
  with members:
    - "deep dynamics models for learning dexterous manipulation"
    - "deep visual mpc policy learning for navigation"
    - "neural autonomous navigation with riemannian motion policy"
    - "composable action conditioned predictors flexible off policy learning for robot navigation"
    - "deep ordinal regression network for monocular depth estimation"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "learning to drive in a day"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

Connected through hyperedge HE-7 (induced by source paper "learning view and target invariant visual servoing for navigation"):
  with members:
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "gibson env real world perception for embodied agents"
    - "superpoint self supervised interest point detection and description"

Connected through hyperedge HE-8 (induced by source paper "technical report reactive semantic planning in unexplored semantic environments using deep perceptual feedback"):
  with members:
    - "reactive navigation in partially familiar planar environments using semantic perceptual feedback"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "neural autonomous navigation with riemannian motion policy"
    - "yolov3 an incremental improvement"
    - "6 dof object pose from semantic keypoints"
    - "high speed flight in an ergodic forest"

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.LG(Machine Learning)