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: "the strands project long term autonomy in everyday environments"
  Abstract: Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance.

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"
    - "robobrain large scale knowledge engine for robots"

Connected through hyperedge HE-2 (induced by source paper "learning to optimize autonomy in competence aware systems"):
  with members:
    - "ivoa introspective vision for obstacle avoidance"
    - "towards adjustable autonomy for the real world"

Connected through hyperedge HE-3 (induced by source paper "are you still with me continuous engagement assessment from a robot s point of view"):
  with members:
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "enabling robots to understand incomplete natural language instructions using commonsense reasoning"
    - "from recognition to cognition visual commonsense reasoning"
    - "spatial knowledge distillation to aid visual reasoning"
    - "iterative visual reasoning beyond convolutions"
    - "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"

Connected through hyperedge HE-5 (induced by source paper "sasse scalable and adaptable 6 dof pose estimation"):
  with members:
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "geometry aware learning of maps for camera localization"
    - "backtracking regression forests for accurate camera relocalization"
    - "image based localization using lstms for structured feature correlation"
    - "direct sparse odometry"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-6 (induced by source paper "desiderata for planning systems in general purpose service robots"):
  with members:
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "improving grounded natural language understanding through human robot dialog"
    - "interaction and autonomy in robocup home and building wide intelligence"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "balancing explicability and explanation in human aware planning"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "a refinement based architecture for knowledge representation and reasoning in robotics"
    - "plan explicability and predictability for robot task planning"

Connected through hyperedge HE-7 (induced by source paper "rescan inductive instance segmentation for indoor rgbd scans"):
  with members:
    - "constructaide analyzing and visualizing construction sites through photographs and building models"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "detection and tracking of general movable objects in large 3d maps"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "matterport3d learning from rgb d data in indoor environments"
    - "learning hierarchical object maps of non stationary environments with mobile robots"

Connected through hyperedge HE-8 (induced by source paper "augmenting knowledge through statistical goal oriented human robot dialog"):
  with members:
    - "goal oriented dialogue policy learning from failures"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "cornell spf cornell semantic parsing framework"
    - "optimizing dialogue management with reinforcement learning experiments with the njfun system"

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)