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: "robobrain large scale knowledge engine for robots"
  Abstract: In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks. Building such an engine brings with it the challenge of dealing with multiple data modalities including symbols, natural language, haptic senses, robot trajectories, visual features and many others. The \textit{knowledge} stored in the engine comes from multiple sources including physical interactions that robots have while performing tasks (perception, planning and control), knowledge bases from the Internet and learned representations from several robotics research groups. #R##N#We discuss various technical aspects and associated challenges such as modeling the correctness of knowledge, inferring latent information and formulating different robotic tasks as queries to the knowledge engine. We describe the system architecture and how it supports different mechanisms for users and robots to interact with the engine. Finally, we demonstrate its use in three important research areas: grounding natural language, perception, and planning, which are the key building blocks for many robotic tasks. This knowledge engine is a collaborative effort and we call it RoboBrain.

Connected through hyperedge HE-1 (induced by source paper "designing interaction for multi agent cooperative system in an office environment"):
  with members:
    - "ai challenges in human robot cognitive teaming"
    - "the strands project long term autonomy in everyday environments"

Connected through hyperedge HE-2 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-3 (induced by source paper "leveraging semantics for incremental learning in multi relational embeddings"):
  with members:
    - "robocse robot common sense embedding"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "generalization to novel objects using prior relational knowledge"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "investigating human priors for playing video games"
    - "quantifying generalization in reinforcement learning"
    - "assessing generalization in deep reinforcement learning"
    - "visual semantic navigation using scene priors"
    - "gotta learn fast a new benchmark for generalization in rl"

Connected through hyperedge HE-5 (induced by source paper "a fog robotics approach to deep robot learning application to object recognition and grasp planning in surface decluttering"):
  with members:
    - "network offloading policies for cloud robotics a learning based approach"
    - "fog robotics for efficient fluent and robust human robot interaction"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "scanning the internet for ros a view of security in robotics research"
    - "a fog robotic system for dynamic visual servoing"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "robocse robot common sense embedding"):
  with members:
    - "matterport3d learning from rgb d data in indoor environments"
    - "visual semantic planning using deep successor representations"
    - "analogical inference for multi relational embeddings"
    - "ai2 thor an interactive 3d environment for visual ai"
    - "semantic scene completion from a single depth image"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "from multi modal property dataset to robot centric conceptual knowledge about household objects"):
  with members:
    - "what stands in for a missing tool a prototypical grounded knowledge based approach to tool substitution"
    - "the symbol grounding problem"

Connected through hyperedge HE-8 (induced by source paper "vision based navigation with language based assistance via imitation learning with indirect intervention"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "virtualhome simulating household activities via programs"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "textworld a learning environment for text based games"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street 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.AI(Artificial Intelligence)