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: "moddrop adaptive multi modal gesture recognition"
  Abstract: We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.

Connected through hyperedge HE-1 (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"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"

Connected through hyperedge HE-2 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-3 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "mfas multimodal fusion architecture search"
    - "task2vec task embedding for meta learning"
    - "best sources forward domain generalization through source specific nets"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-4 (induced by source paper "learning with privileged information via adversarial discriminative modality distillation"):
  with members:
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "modality distillation with multiple stream networks for action recognition"
    - "adversarial network compression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "mfas multimodal fusion architecture search"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "centralnet a multilayer approach for multimodal fusion"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "efficient progressive neural architecture search"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-6 (induced by source paper "deep aligned convolutional neural network for skeleton based action recognition and segmentation"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "skeleton based action recognition using lstm and cnn"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "convolutional sequence to sequence learning"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-7 (induced by source paper "dynamic gesture recognition by using cnns and star rgb a temporal information condensation"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "convolutional two stream network fusion for video action recognition"
    - "beyond temporal pooling recurrence and temporal convolutions for gesture recognition in video"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "progression modelling for online and early gesture detection"):
  with members:
    - "encouraging lstms to anticipate actions very early"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "large scale continuous gesture recognition using convolutional neural networks"
    - "beyond temporal pooling recurrence and temporal convolutions for gesture recognition in video"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "efficient object localization using convolutional networks"
    - "learning spatiotemporal features with 3d convolutional networks"

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)