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: "seeing what you re told sentence guided activity recognition in video"
  Abstract: We present a system that demonstrates how the compositional structure of events, in concert with the compositional structure of language, can interplay with the underlying focusing mechanisms in video action recognition, thereby providing a medium, not only for top-down and bottom-up integration, but also for multi-modal integration between vision and language. We show how the roles played by participants (nouns), their characteristics (adjectives), the actions performed (verbs), the manner of such actions (adverbs), and changing spatial relations between participants (prepositions) in the form of whole sentential descriptions mediated by a grammar, guides the activity-recognition process. Further, the utility and expressiveness of our framework is demonstrated by performing three separate tasks in the domain of multi-activity videos: sentence-guided focus of attention, generation of sentential descriptions of video, and query-based video search, simply by leveraging the framework in different manners.

Connected through hyperedge HE-1 (induced by source paper "visually grounded neural syntax acquisition"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "cooperative learning of disjoint syntax and semantics"
    - "on tree based neural sentence modeling"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "straight to the tree constituency parsing with neural syntactic distance"

Connected through hyperedge HE-2 (induced by source paper "curvature a signature for action recognition in video sequences"):
  with members:
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "signal alignment for humanoid skeletons via the globally optimal reparameterization algorithm"
    - "attentional pooling for action recognition"
    - "mask r cnn"
    - "temporal relational reasoning in videos"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-3 (induced by source paper "deep sequential models for sampling based planning"):
  with members:
    - "inference compilation and universal probabilistic programming"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "monte carlo motion planning for robot trajectory optimization under uncertainty"
    - "detecting events and key actors in multi person videos"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "simultaneous object detection tracking and event recognition"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal person retrieval via natural language queries"):
  with members:
    - "person search with natural language description"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "grounding of textual phrases in images by reconstruction"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"
    - "learning deep structure preserving image text embeddings"
    - "skip thought vectors"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "space time representation of people based on 3d skeletal data a review"):
  with members:
    - "simultaneous feature and body part learning for real time robot awareness of human behaviors"
    - "kernelized covariance for action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "linear time online action detection from 3d skeletal data using bags of gesturelets"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
    - "cross view action modeling learning and recognition"

Connected through hyperedge HE-6 (induced by source paper "do you see what i mean visual resolution of linguistic ambiguities"):
  with members:
    - "combining language and vision with a multimodal skip gram model"
    - "translating videos to natural language using deep recurrent neural networks"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"
    - "show and tell a neural image caption generator"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-7 (induced by source paper "collecting and annotating the large continuous action dataset"):
  with members:
    - "beyond short snippets deep networks for video classification"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "video in sentences out"
    - "imagenet large scale visual recognition challenge"
    - "a discriminative cnn video representation for event detection"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "simultaneous object detection tracking and event recognition"

Connected through hyperedge HE-8 (induced by source paper "a faster method for tracking and scoring videos corresponding to sentences"):
  with members:
    - "simultaneous object detection tracking and event recognition"

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)