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: "phrase localization and visual relationship detection with comprehensive linguistic cues"
  Abstract: This paper presents a framework for localization or grounding of phrases in images using a large collection of linguistic and visual cues. We model the appearance, size, and position of entity bounding boxes, adjectives that contain attribute information, and spatial relationships between pairs of entities connected by verbs or prepositions. We pay special attention to relationships between people and clothing or body part mentions, as they are useful for distinguishing individuals. We automatically learn weights for combining these cues and at test time, perform joint inference over all phrases in a caption. The resulting system produces a 5% improvement in accuracy over the state of the art on phrase localization on the Flickr30k Entities dataset and a 4-10% improvement for visual relationship detection on the Stanford VRD dataset.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "dense relational captioning triple stream networks for relationship based captioning"):
  with members:
    - "linknet relational embedding for scene graph"
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "inferring and executing programs for visual reasoning"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-4 (induced by source paper "object referring in videos with language and human gaze"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "end to end representation learning for correlation filter based tracking"
    - "query guided regression network with context policy for phrase grounding"
    - "object referring in visual scene with spoken language"
    - "localizing moments in video with natural language"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "detecting unseen visual relations using analogies"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "large scale visual relationship understanding"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "a simple neural network module for relational reasoning"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "relnet end to end modeling of entities relations"

Connected through hyperedge HE-6 (induced by source paper "factorizable net an efficient subgraph based framework for scene graph generation"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "towards context aware interaction recognition"
    - "visual question generation as dual task of visual question answering"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"

Connected through hyperedge HE-7 (induced by source paper "referring relationships"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"
    - "grounding spatio semantic referring expressions for human robot interaction"
    - "convolutional 2d knowledge graph embeddings"
    - "visual translation embedding network for visual relation detection"

Connected through hyperedge HE-8 (induced by source paper "dock detecting objects by transferring common sense knowledge"):
  with members:
    - "visir visual and semantic image label refinement"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "the more you know using knowledge graphs for image classification"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "visual relationship detection with language priors"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "weakly supervised deep detection networks"
    - "fast r cnn"

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)