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: "dual glance model for deciphering social relationships"
  Abstract: Since the beginning of early civilizations, social relationships derived from each individual fundamentally form the basis of social structure in our daily life. In the computer vision literature, much progress has been made in scene understanding, such as object detection and scene parsing. Recent research focuses on the relationship between objects based on its functionality and geometrical relations. In this work, we aim to study the problem of social relationship recognition, in still images. We have proposed a dual-glance model for social relationship recognition, where the first glance fixates at the individual pair of interest and the second glance deploys attention mechanism to explore contextual cues. We have also collected a new large scale People in Social Context (PISC) dataset, which comprises of 22,670 images and 76,568 annotated samples from 9 types of social relationship. We provide benchmark results on the PISC dataset, and qualitatively demonstrate the efficacy of the proposed model.

Connected through hyperedge HE-1 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "ffnet video fast forwarding via reinforcement learning"
    - "predicting visual features from text for image and video caption retrieval"
    - "unsupervised learning of view invariant action representations"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "improved image captioning via policy gradient optimization of spider"
    - "summarization of user generated sports video by using deep action recognition features"
    - "attention transfer from web images for video recognition"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised multi person action recognition in 360 circ videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unsupervised learning of view invariant action representations"
    - "improved person detection on omnidirectional images with non maxima suppression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-3 (induced by source paper "direction concentration learning enhancing congruency in machine learning"):
  with members:
    - "learning to detect"
    - "a dilated inception network for visual saliency prediction"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "natural gradient deep q learning"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "gradient episodic memory for continual learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-4 (induced by source paper "multi granularity reasoning for social relation recognition from images"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "simple baselines for human pose estimation and tracking"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a domain based approach to social relation recognition"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"
    - "from facial expression recognition to interpersonal relation prediction"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "an end to end network for generating social relationship graphs"):
  with members:
    - "person recognition in personal photo collections"
    - "a domain based approach to social relation recognition"
    - "neural motifs scene graph parsing with global context"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "gated graph sequence neural networks"
    - "spice semantic propositional image caption evaluation"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "visual social relationship recognition"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "deep label distribution learning with label ambiguity"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "focal loss for dense object detection"
    - "a domain based approach to social relation recognition"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "moviegraphs towards understanding human centric situations from videos"
    - "scene graph generation from objects phrases and region captions"

Connected through hyperedge HE-7 (induced by source paper "interact as you intend intention driven human object interaction detection"):
  with members:
    - "referring relationships"
    - "ican instance centric attention network for human object interaction detection"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "inferring and executing programs for visual reasoning"
    - "an implementation of faster rcnn with study for region sampling"
    - "learning to detect human object interactions"
    - "scene graph generation from objects phrases and region captions"

Connected through hyperedge HE-8 (induced by source paper "deep reasoning with knowledge graph for social relationship understanding"):
  with members:
    - "a domain based approach to social relation recognition"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "hadamard product for low rank bilinear pooling"
    - "semantic object parsing with graph lstm"
    - "gated graph sequence neural networks"
    - "graph structured representations for visual question answering"

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)