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: "robot in a room toward perfect object recognition in closed environments"
  Abstract: While general object recognition is still far from being solved, this paper proposes a way for a robot to recognize every object at an almost human-level accuracy. Our key observation is that many robots will stay in a relatively closed environment (e.g. a house or an office). By constraining a robot to stay in a limited territory, we can ensure that the robot has seen most objects before and the speed of introducing a new object is slow. Furthermore, we can build a 3D map of the environment to reliably subtract the background to make recognition easier. We propose extremely robust algorithms to obtain a 3D map and enable humans to collectively annotate objects. During testing time, our algorithm can recognize all objects very reliably, and query humans from crowd sourcing platform if confidence is low or new objects are identified. This paper explains design decisions in building such a system, and constructs a benchmark for extensive evaluation. Experiments suggest that making robot vision appear to be working from an end user's perspective is a reachable goal today, as long as the robot stays in a closed environment. By formulating this task, we hope to lay the foundation of a new direction in vision for robotics. Code and data will be available upon acceptance.

Connected through hyperedge HE-1 (induced by source paper "active scene understanding via online semantic reconstruction"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "object aware guidance for autonomous scene reconstruction"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-2 (induced by source paper "robust place categorization with deep domain generalization"):
  with members:
    - "autodial automatic domain alignment layers"
    - "learning deep nbnn representations for robust place categorization"
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "deep learning features at scale for visual place recognition"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "imagenet pre trained models with batch normalization"
    - "revisiting batch normalization for practical domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "cut paste and learn surprisingly easy synthesis for instance detection"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "a dataset for developing and benchmarking active vision"
    - "learning from synthetic humans"
    - "synthesizing training data for object detection in indoor scenes"
    - "how useful is photo realistic rendering for visual learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-4 (induced by source paper "a dataset for developing and benchmarking active vision"):
  with members:
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "rgbd datasets past present and future"
    - "ssd single shot multibox detector"
    - "multiview rgb d dataset for object instance detection"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "fast single shot detection and pose estimation"
    - "you only look once unified real time object detection"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-5 (induced by source paper "synthesizing training data for object detection in indoor scenes"):
  with members:
    - "ssd single shot multibox detector"
    - "multiview rgb d dataset for object instance detection"
    - "joint semantic segmentation and depth estimation with deep convolutional networks"
    - "playing for data ground truth from computer games"
    - "synthetic data for text localisation in natural images"
    - "fast r cnn"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "slam with objects using a nonparametric pose graph"):
  with members:
    - "monocular slam supported object recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "imagenet large scale visual recognition challenge"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "occupancy grids a stochastic spatial representation for active robot perception"
    - "revisiting k means new algorithms via bayesian nonparametrics"

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)