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: "ppr fcn weakly supervised visual relation detection via parallel pairwise r fcn"
  Abstract: We aim to tackle a novel vision task called Weakly Supervised Visual Relation Detection (WSVRD) to detect "subject-predicate-object" relations in an image with object relation groundtruths available only at the image level. This is motivated by the fact that it is extremely expensive to label the combinatorial relations between objects at the instance level. Compared to the extensively studied problem, Weakly Supervised Object Detection (WSOD), WSVRD is more challenging as it needs to examine a large set of regions pairs, which is computationally prohibitive and more likely stuck in a local optimal solution such as those involving wrong spatial context. To this end, we present a Parallel, Pairwise Region-based, Fully Convolutional Network (PPR-FCN) for WSVRD. It uses a parallel FCN architecture that simultaneously performs pair selection and classification of single regions and region pairs for object and relation detection, while sharing almost all computation shared over the entire image. In particular, we propose a novel position-role-sensitive score map with pairwise RoI pooling to efficiently capture the crucial context associated with a pair of objects. We demonstrate the superiority of PPR-FCN over all baselines in solving the WSVRD challenge by using results of extensive experiments over two visual relation benchmarks.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised visual semantic parsing"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "learning human object interactions by graph parsing neural networks"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "explainable and explicit visual reasoning over scene graphs"

Connected through hyperedge HE-2 (induced by source paper "unbiased scene graph generation via rich and fair semantic extraction"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "graph r cnn for scene graph generation"
    - "attentive relational networks for mapping images to scene graphs"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-3 (induced by source paper "avr attention based salient visual relationship detection"):
  with members:
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "deep group shuffling random walk for person re identification"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "exploring visual relationship for image captioning"
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-4 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-5 (induced by source paper "learning visual relation priors for image text matching and image captioning with neural scene graph generators"):
  with members:
    - "relational reasoning using prior knowledge for visual captioning"
    - "rethinking visual relationships for high level image understanding"
    - "object driven text to image synthesis via adversarial training"
    - "vrr vg refocusing visually relevant relationships"
    - "scene graph reasoning with prior visual relationship for visual question answering"
    - "stacked cross attention for image text matching"
    - "multi modal learning with prior visual relation reasoning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-6 (induced by source paper "what object should i use task driven object detection"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "graph r cnn for scene graph generation"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "videos as space time region graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "soft nms improving object detection with one line of code"
    - "visual question answering as a meta learning task"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-7 (induced by source paper "variational context exploiting visual and textual context for grounding referring expressions"):
  with members:
    - "you only look listen once towards fast and accurate visual grounding"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "vip cnn visual phrase guided convolutional neural network"
    - "grounding referring expressions in images by variational context"
    - "a domain based approach to social relation recognition"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "comprehension guided referring expressions"

Connected through hyperedge HE-8 (induced by source paper "graphical contrastive losses for scene graph parsing"):
  with members:
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "mattnet modular attention network for referring expression comprehension"
    - "large scale visual relationship understanding"
    - "an interpretable model for scene graph generation"
    - "query guided regression network with context policy for phrase grounding"

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)