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: "spatio temporal person retrieval via natural language queries"
  Abstract: In this paper, we address the problem of spatio-temporal person retrieval from multiple videos using a natural language query, in which we output a tube (i.e., a sequence of bounding boxes) which encloses the person described by the query. For this problem, we introduce a novel dataset consisting of videos containing people annotated with bounding boxes for each second and with five natural language descriptions. To retrieve the tube of the person described by a given natural language query, we design a model that combines methods for spatio-temporal human detection and multimodal retrieval. We conduct comprehensive experiments to compare a variety of tube and text representations and multimodal retrieval methods, and present a strong baseline in this task as well as demonstrate the efficacy of our tube representation and multimodal feature embedding technique. Finally, we demonstrate the versatility of our model by applying it to two other important tasks.

Connected through hyperedge HE-1 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences"):
  with members:
    - "dynamic graph attention for referring expression comprehension"
    - "weakly supervised video moment retrieval from text queries"
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "relation aware graph attention network for visual question answering"
    - "cross modal relationship inference for grounding referring expressions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised spatio temporally grounding natural sentence in video"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "motion appearance co memory networks for video question answering"
    - "object referring in videos with language and human gaze"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"

Connected through hyperedge HE-3 (induced by source paper "grounding tracking integration"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "zero shot grounding of objects from natural language queries"
    - "mask scoring r cnn"
    - "atom accurate tracking by overlap maximization"
    - "yolov3 an incremental improvement"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"

Connected through hyperedge HE-4 (induced by source paper "referring to objects in videos using spatio temporal identifying descriptions"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "visual referring expression recognition what do systems actually learn"
    - "tvqa localized compositional video question answering"
    - "actor and action video segmentation from a sentence"
    - "flow guided feature aggregation for video object detection"
    - "obj2text generating visually descriptive language from object layouts"
    - "dense captioning events in videos"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

Connected through hyperedge HE-5 (induced by source paper "grounded video description"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "move forward and tell a progressive generator of video descriptions"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"

Connected through hyperedge HE-6 (induced by source paper "actor and action video segmentation from a sentence"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "person search with natural language description"
    - "temporal action detection with structured segment networks"
    - "localizing actions from video labels and pseudo annotations"
    - "the kinetics human action video dataset"
    - "tall temporal activity localization via language query"
    - "spatial aware object embeddings for zero shot localization and classification of actions"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-7 (induced by source paper "multi modal capsule routing for actor and action video segmentation conditioned on natural language queries"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "actor and action video segmentation from a sentence"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dynamic routing between capsules"
    - "action tubelet detector for spatio temporal action localization"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "tall temporal activity localization via language query"

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)