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: "understand scene categories by objects a semantic regularized scene classifier using convolutional neural networks"
  Abstract: Scene classification is a fundamental perception task for environmental understanding in today's robotics. In this paper, we have attempted to exploit the use of popular machine learning technique of deep learning to enhance scene understanding, particularly in robotics applications. As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples. Inspired by human scene understanding based on object knowledge, we address the problem of scene classification by encouraging deep neural networks to incorporate object-level information. This is implemented with a regularization of semantic segmentation. With only 5 thousand training images, as opposed to 2.5 million images, we show the proposed deep architecture achieves superior scene classification results to the state-of-the-art on a publicly available SUN RGB-D dataset. In addition, performance of semantic segmentation, the regularizer, also reaches a new record with refinement derived from predicted scene labels. Finally, we apply our SUN RGB-D dataset trained model to a mobile robot captured images to classify scenes in our university demonstrating the generalization ability of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "multi task learning with coarse priors for robust part aware person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "cdpm convolutional deformable part models for person re identification"
    - "relation aware global attention"
    - "multi level factorisation net for person re identification"
    - "discriminative feature learning with foreground attention for person re identification"
    - "attention aware compositional network for person re identification"
    - "factorized distillation training holistic person re identification model by distilling an ensemble of partial reid models"
    - "dual attention matching network for context aware feature sequence based person re identification"

Connected through hyperedge HE-2 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi view intact space learning"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "theory of deep learning iib optimization properties of sgd"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "soft ranking label encoding for robust facial age estimation"):
  with members:
    - "bridgenet a continuity aware probabilistic network for age estimation"
    - "c3ae exploring the limits of compact model for age estimation"
    - "deep representation learning with part loss for person re identification"
    - "fine grained age estimation in the wild with attention lstm networks"
    - "a coupled evolutionary network for age estimation"
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "deep label distribution learning with label ambiguity"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

Connected through hyperedge HE-4 (induced by source paper "deduce diverse scene detection methods in unseen challenging environments"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "robust place categorization with deep domain generalization"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "centroid based scene classification cbsc using deep features and clustering for rgb d indoor scene classification"):
  with members:
    - "learning effective rgb d representations for scene recognition"
    - "clustering with deep learning taxonomy and new methods"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "joint unsupervised learning of deep representations and image clusters"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "exploiting local features from deep networks for image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "cbcl brain inspired model for rgb d indoor scene classification"):
  with members:
    - "learning effective rgb d representations for scene recognition"
    - "clustering with deep learning taxonomy and new methods"
    - "deep metric learning and image classification with nearest neighbour gaussian kernels"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "the mythos of model interpretability"
    - "joint unsupervised learning of deep representations and image clusters"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "efficient coarse to fine non local module for the detection of small objects"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "ocnet object context network for scene parsing"
    - "yolov3 an incremental improvement"
    - "compact generalized non local network"
    - "auto context r cnn"
    - "relational inductive biases deep learning and graph networks"
    - "non local recurrent network for image restoration"
    - "couplenet coupling global structure with local parts for object detection"

Connected through hyperedge HE-8 (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"

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)