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: "zero shot activity recognition with verb attribute induction"
  Abstract: In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb "salute" has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between visual and textual representations to reason about a previously unseen action. In contrast to much prior work that assumes access to gold standard attributes for zero-shot classes and focuses primarily on object attributes, our model uniquely learns to infer action attributes from dictionary definitions and distributed word representations. Experimental results confirm that action attributes inferred from language can provide a predictive signal for zero-shot prediction of previously unseen activities.

Connected through hyperedge HE-1 (induced by source paper "zero shot activity recognition with videos"):
  with members:
    - "human action recognition and prediction a survey"
    - "unseen action recognition with multimodal learning"
    - "jointly localizing and describing events for dense video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint event detection and description in continuous video streams"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "tall temporal activity localization via language query"

Connected through hyperedge HE-2 (induced by source paper "action modifiers learning from adverbs in instructional videos"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "weakly supervised video moment retrieval from text queries"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "human action adverb recognition adha dataset and a three stream hybrid model"
    - "adversarial soft detection based aggregation network for image retrieval"
    - "motion appearance co memory networks for video question answering"
    - "attributes as operators factorizing unseen attribute object compositions"

Connected through hyperedge HE-3 (induced by source paper "learning visual actions using multiple verb only labels"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "scaling egocentric vision the epic kitchens dataset"
    - "end to end dense video captioning with masked transformer"
    - "reconstruction network for video captioning"
    - "what actions are needed for understanding human actions in videos"
    - "video captioning via hierarchical reinforcement learning"
    - "convolutional image captioning"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-4 (induced by source paper "learning to compose topic aware mixture of experts for zero shot video captioning"):
  with members:
    - "neural baby talk"
    - "universal neural machine translation for extremely low resource languages"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "decoupled novel object captioner"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-5 (induced by source paper "multimodal grounding for language processing"):
  with members:
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "interactive natural language acquisition in a multi modal recurrent neural architecture"
    - "multimodal named entity recognition for short social media posts"
    - "modulating early visual processing by language"
    - "multimodal machine learning a survey and taxonomy"
    - "gated attention architectures for task oriented language grounding"
    - "emergence of grounded compositional language in multi agent populations"
    - "teaching machines to describe images via natural language feedback"

Connected through hyperedge HE-6 (induced by source paper "similarity r c3d for few shot temporal activity detection"):
  with members:
    - "a generative approach to zero shot and few shot action recognition"
    - "autoloc weakly supervised temporal action localization"
    - "visual data synthesis via gan for zero shot video classification"
    - "temporal action detection with structured segment networks"
    - "prototypical networks for few shot learning"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-7 (induced by source paper "secaps a sequence enhanced capsule model for charge prediction"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "focal loss for dense object detection"
    - "dynamic routing between capsules"
    - "learning to predict charges for criminal cases with legal basis"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "zero shot recognition with unreliable attributes"

Connected through hyperedge HE-8 (induced by source paper "neural motifs scene graph parsing with global context"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "an implementation of faster rcnn with study for region sampling"
    - "neural amr sequence to sequence models for parsing and generation"
    - "learning to reason end to end module networks for visual question answering"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "obj2text generating visually descriptive language from object layouts"

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.CL(Computation and Language)