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: "attributes as operators factorizing unseen attribute object compositions"
  Abstract: We present a new approach to modeling visual attributes. Prior work casts attributes in a similar role as objects, learning a latent representation where properties (e.g., sliced) are recognized by classifiers much in the way objects (e.g., apple) are. However, this common approach fails to separate the attributes observed during training from the objects with which they are composed, making it ineffectual when encountering new attribute-object compositions. Instead, we propose to model attributes as operators. Our approach learns a semantic embedding that explicitly factors out attributes from their accompanying objects, and also benefits from novel regularizers expressing attribute operators' effects (e.g., blunt should undo the effects of sharp). Not only does our approach align conceptually with the linguistic role of attributes as modifiers, but it also generalizes to recognize unseen compositions of objects and attributes. We validate our approach on two challenging datasets and demonstrate significant improvements over the state-of-the-art. In addition, we show that not only can our model recognize unseen compositions robustly in an open-world setting, it can also generalize to compositions where objects themselves were unseen during training.

Connected through hyperedge HE-1 (induced by source paper "learning to learn words from visual scenes"):
  with members:
    - "compositional generalization in image captioning"
    - "few shot representation learning for out of vocabulary words"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "pointing novel objects in image captioning"

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"
    - "w talc weakly supervised temporal activity localization and classification"

Connected through hyperedge HE-3 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "learning to evaluate image captioning"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "visual translation embedding network for visual relation detection"

Connected through hyperedge HE-4 (induced by source paper "laso label set operations networks for multi label few shot learning"):
  with members:
    - "compositional gan learning conditional image composition"
    - "deep meta learning learning to learn in the concept space"
    - "the elephant in the room"
    - "compositional gan learning image conditional binary composition"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot learning"
    - "multi label zero shot learning with structured knowledge graphs"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "learning to learn words from narrated video"):
  with members:
    - "compositional generalization in image captioning"
    - "few shot representation learning for out of vocabulary words"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "compositional generalization through meta sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "task aware deep sampling for feature generation"):
  with members:
    - "tafe net task aware feature embeddings for low shot learning"
    - "low shot learning via covariance preserving adversarial augmentation networks"
    - "multi modal cycle consistent generalized zero shot learning"
    - "a style based generator architecture for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "deep layer aggregation"

Connected through hyperedge HE-7 (induced by source paper "plan arithmetic compositional plan vectors for multi task control"):
  with members:
    - "hierarchical visuomotor control of humanoids"
    - "composable deep reinforcement learning for robotic manipulation"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "latent space policies for hierarchical reinforcement learning"
    - "task embedded control networks for few shot imitation learning"
    - "neural probabilistic motor primitives for humanoid control"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-8 (induced by source paper "task driven modular networks for zero shot compositional learning"):
  with members:
    - "from classical to generalized zero shot learning a simple adaptation process"
    - "houdini lifelong learning as program synthesis"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "maskconnect connectivity learning by gradient descent"
    - "the devil is in the tails fine grained classification in the wild"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "film visual reasoning with a general conditioning layer"
    - "pathnet evolution channels gradient descent in super neural networks"

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)