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: "choosing smartly adaptive multimodal fusion for object detection in changing environments"
  Abstract: Object detection is an essential task for autonomous robots operating in dynamic and changing environments. A robot should be able to detect objects in the presence of sensor noise that can be induced by changing lighting conditions for cameras and false depth readings for range sensors, especially RGB-D cameras. To tackle these challenges, we propose a novel adaptive fusion approach for object detection that learns weighting the predictions of different sensor modalities in an online manner. Our approach is based on a mixture of convolutional neural network (CNN) experts and incorporates multiple modalities including appearance, depth and motion. We test our method in extensive robot experiments, in which we detect people in a combined indoor and outdoor scenario from RGB-D data, and we demonstrate that our method can adapt to harsh lighting changes and severe camera motion blur. Furthermore, we present a new RGB-D dataset for people detection in mixed in- and outdoor environments, recorded with a mobile robot.

Connected through hyperedge HE-1 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robust video content alignment and compensation for clear vision through the rain"
    - "residual dense network for image super resolution"
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"

Connected through hyperedge HE-2 (induced by source paper "input dropout for spatially aligned modalities"):
  with members:
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "semantic foggy scene understanding with synthetic data"
    - "modality distillation with multiple stream networks for action recognition"
    - "a framework for evaluating 6 dof object trackers"
    - "focal loss for dense object detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "gated2depth real time dense lidar from gated images"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robust video content alignment and compensation for clear vision through the rain"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (induced by source paper "learning the depths of moving people by watching frozen people"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "soccer on your tabletop"

Connected through hyperedge HE-5 (induced by source paper "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"):
  with members:
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "when will you do what anticipating temporal occurrences of activities"
    - "lsta long short term attention for egocentric action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "forecasting hand and object locations in future frames"
    - "am i done predicting action progress in videos"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-6 (induced by source paper "on single source robustness in deep fusion models"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "robust deep multi modal learning based on gated information fusion network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "state of the art speech recognition with sequence to sequence models"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "a deep metric for multimodal registration"

Connected through hyperedge HE-7 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "on single source robustness in deep fusion models"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep neural networks with auxiliary model regulated gating for resilient multi modal sensor fusion"):
  with members:
    - "robust deep multi modal learning based on gated information fusion network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "sensor fusion for robot control through deep reinforcement learning"
    - "brain4cars car that knows before you do via sensory fusion deep learning architecture"
    - "multi view 3d object detection network for autonomous driving"
    - "fast r cnn"
    - "unstructured human activity detection from rgbd images"

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)