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: "generating holistic 3d scene abstractions for text based image retrieval"
  Abstract: Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, using 3D spatial relationships rather than 2D relationships that assume a particular viewing direction, one of the main challenges is to infer the 3D structure that bridges images with users' text descriptions. However, direct inference of 3D structure from images requires learning from large scale annotated data. Since interactions between objects can be reduced to a limited set of atomic spatial relations in 3D, we study the possibility of inferring 3D structure from a text description rather than an image, applying physical relation models to synthesize holistic 3D abstract object layouts satisfying the spatial constraints present in a textual description. We present a generic framework for retrieving images from a textual description of a scene by matching images with these generated abstract object layouts. Images are ranked by matching object detection outputs (bounding boxes) to 2D layout candidates (also represented by bounding boxes) which are obtained by projecting the 3D scenes with sampled camera directions. We validate our approach using public indoor scene datasets and show that our method outperforms baselines built upon object occurrence histograms and learned 2D pairwise relations.

Connected through hyperedge HE-1 (induced by source paper "modeling local geometric structure of 3d point clouds using geo cnn"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "multiresolution tree networks for 3d point cloud processing"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "local spectral graph convolution for point set feature learning"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "fully convolutional point networks for large scale point clouds"

Connected through hyperedge HE-2 (induced by source paper "representing videos based on scene layouts for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "c wsl count guided weakly supervised localization"

Connected through hyperedge HE-3 (induced by source paper "layout induced video representation for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "remotenet efficient relevant motion event detection for large scale home surveillance videos"):
  with members:
    - "viton an image based virtual try on network"
    - "nisp pruning networks using neuron importance score propagation"
    - "c wsl count guided weakly supervised localization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "dynamic zoom in network for fast object detection in large images"
    - "an overview of multi task learning in deep neural networks"
    - "deep tracking seeing beyond seeing using recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi perspective relevance matching with hierarchical convnets for social media search"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "talking to your tv context aware voice search with hierarchical recurrent neural networks"
    - "pacrr a position aware neural ir model for relevance matching"
    - "exploring the effectiveness of convolutional neural networks for answer selection in end to end question answering"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "text matching as image recognition"

Connected through hyperedge HE-6 (induced by source paper "nisp pruning networks using neuron importance score propagation"):
  with members:
    - "learning rich features for image manipulation detection"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "viton an image based virtual try on network"
    - "c wsl count guided weakly supervised localization"
    - "blockdrop dynamic inference paths in residual networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "deep regionlets for object detection"

Connected through hyperedge HE-7 (induced by source paper "dynamic zoom in network for fast object detection in large images"):
  with members:
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "nisp pruning networks using neuron importance score propagation"
    - "c wsl count guided weakly supervised localization"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "the role of context selection in object detection"

Connected through hyperedge HE-8 (induced by source paper "visual relationship detection with internal and external linguistic knowledge distillation"):
  with members:
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "harnessing deep neural networks with logic rules"
    - "phrase localization and visual relationship detection with comprehensive linguistic cues"
    - "the role of context selection in object detection"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "modeling context between objects for referring expression understanding"
    - "visual relationship detection with language priors"

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)