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: "alternative semantic representations for zero shot human action recognition"
  Abstract: A proper semantic representation for encoding side information is key to the success of zero-shot learning. In this paper, we explore two alternative semantic representations especially for zero-shot human action recognition: textual descriptions of human actions and deep features extracted from still images relevant to human actions. Such side information are accessible on Web with little cost, which paves a new way in gaining side information for large-scale zero-shot human action recognition. We investigate different encoding methods to generate semantic representations for human actions from such side information. Based on our zero-shot visual recognition method, we conducted experiments on UCF101 and HMDB51 to evaluate two proposed semantic representations . The results suggest that our proposed text- and image-based semantic representations outperform traditional attributes and word vectors considerably for zero-shot human action recognition. In particular, the image-based semantic representations yield the favourable performance even though the representation is extracted from a small number of images per class.

Connected through hyperedge HE-1 (induced by source paper "crl class representative learning for image classification"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "meta transfer networks for zero shot learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards universal representation for unseen action recognition"
    - "complementary attributes a new clue to zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "action recognition with spatial temporal discriminative filter banks"
    - "divide and conquer the embedding space for metric learning"
    - "action2vec a crossmodal embedding approach to action learning"
    - "cross and learn cross modal self supervision"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "unifying unsupervised domain adaptation and zero shot visual recognition"):
  with members:
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "conditional adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "zero shot sign language recognition can textual data uncover sign languages"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "towards universal representation for unseen action recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fine grained object recognition and zero shot learning in remote sensing imagery"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-5 (induced by source paper "attributes as operators factorizing unseen attribute object compositions"):
  with members:
    - "neural algebra of classifiers"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "joint discovery of object states and manipulation actions"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "visual translation embedding network for visual relation detection"

Connected through hyperedge HE-6 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"
    - "guided optical flow learning"
    - "image de raining using a conditional generative adversarial network"
    - "spatial aware object embeddings for zero shot localization and classification of actions"

Connected through hyperedge HE-7 (induced by source paper "attributes as operators"):
  with members:
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "joint discovery of object states and manipulation actions"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "visual translation embedding network for visual relation detection"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"

Connected through hyperedge HE-8 (induced by source paper "attributes importance for zero shot pose classification based on wearable sensors"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "densepose dense human pose estimation in the wild"
    - "prototypical networks for few shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "semantic embedding space for zero shot action recognition"
    - "zero shot recognition with unreliable attributes"
    - "zero shot learning through cross modal transfer"

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)