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: "probabilistic and or attribute grouping for zero shot learning"
  Abstract: In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given semantic information about the class, like a textual description or a set of attributes. Learning from attributes could benefit from explicitly modeling structure of the attribute space. Unfortunately, learning of general structure from empirical samples is hard with typical dataset sizes. #R##N#Here we describe LAGO, a probabilistic model designed to capture natural soft and-or relations across groups of attributes. We show how this model can be learned end-to-end with a deep attribute-detection model. The soft group structure can be learned from data jointly as part of the model, and can also readily incorporate prior knowledge about groups if available. The soft and-or structure succeeds to capture meaningful and predictive structures, improving the accuracy of zero-shot learning on two of three benchmarks. #R##N#Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP (Lampert et al., 2009) and ESZSL (Romera-Paredes & Torr, 2015). Interestingly, taking only one singleton group for each attribute, introduces a new soft-relaxation of DAP, that outperforms DAP by ~40.

Connected through hyperedge HE-1 (induced by source paper "clarel classification via retrieval loss for zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "zero shot image recognition using relational matching adaptation and calibration"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "multi modal cycle consistent generalized zero shot learning"
    - "adaptive confidence smoothing for generalized zero shot learning"
    - "learning robust visual semantic embeddings"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-2 (induced by source paper "domain aware generalized zero shot learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "multi modal cycle consistent generalized zero shot learning"
    - "zero shot kernel learning"
    - "preserving semantic relations for zero shot learning"
    - "model selection for generalized zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "adaptive confidence smoothing for generalized zero shot learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "multi modal cycle consistent generalized zero shot learning"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "zero shot kernel learning"
    - "model selection for generalized zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

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)