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: "robocse robot common sense embedding"
  Abstract: Autonomous service robots require computational frameworks that allow them to generalize knowledge to new situations in a manner that models uncertainty while scaling to real-world problem sizes. The Robot Common Sense Embedding (RoboCSE) showcases a class of computational frameworks, multi-relational embeddings, that have not been leveraged in robotics to model semantic knowledge. We validate RoboCSE on a realistic home environment simulator (AI2Thor) to measure how well it generalizes learned knowledge about object affordances, locations, and materials. Our experiments show that RoboCSE can perform prediction better than a baseline that uses pre-trained embeddings, such as Word2Vec, achieving statistically significant improvements while using orders of magnitude less memory than our Bayesian Logic Network baseline. In addition, we show that predictions made by RoboCSE are robust to significant reductions in data available for training as well as domain transfer to MatterPort3D, achieving statistically significant improvements over a baseline that memorizes training data.

Connected through hyperedge HE-1 (induced by source paper "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"):
  with members:
    - "what object should i use task driven object detection"
    - "scene graph generation with external knowledge and image reconstruction"
    - "recent advances in imitation learning from observation"
    - "integrating knowledge and reasoning in image understanding"
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep learning for generic object detection a survey"

Connected through hyperedge HE-2 (induced by source paper "leveraging semantics for incremental learning in multi relational embeddings"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "continuous learning in single incremental task scenarios"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "deep generative dual memory network for continual learning"
    - "memory efficient experience replay for streaming learning"
    - "progressive neural networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-3 (induced by source paper "improving robot success detection using static object data"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "a simple neural network module for relational reasoning"
    - "learning interpretable spatial operations in a rich 3d blocks world"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"

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.RO(Robotics)