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: "open world visual recognition using knowledge graphs"
  Abstract: In a real-world setting, visual recognition systems can be brought to make predictions for images belonging to previously unknown class labels. In order to make semantically meaningful predictions for such inputs, we propose a two-step approach that utilizes information from knowledge graphs. First, a knowledge-graph representation is learned to embed a large set of entities into a semantic space. Second, an image representation is learned to embed images into the same space. Under this setup, we are able to predict structured properties in the form of relationship triples for any open-world image. This is true even when a set of labels has been omitted from the training protocols of both the knowledge graph and image embeddings. Furthermore, we append this learning framework with appropriate smoothness constraints and show how prior knowledge can be incorporated into the model. Both these improvements combined increase performance for visual recognition by a factor of six compared to our baseline. Finally, we propose a new, extended dataset which we use for experiments.

Connected through hyperedge HE-1 (induced by source paper "visual and semantic prototypes jointly guided cnn for generalized zero shot learning"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "reducing network agnostophobia"
    - "robust classification with convolutional prototype learning"
    - "zero shot kernel learning"
    - "towards effective deep embedding for zero shot learning"
    - "bi adversarial auto encoder for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "multi stage deep classifier cascades for open world recognition"):
  with members:
    - "robust regression via online feature selection under adversarial data corruption"
    - "open world learning and application to product classification"
    - "learning deep features for one class classification"
    - "random warping series a random features method for time series embedding"
    - "progressive neural networks"
    - "similarity preserving representation learning for time series clustering"
    - "icarl incremental classifier and representation learning"
    - "online open world recognition"

Connected through hyperedge HE-3 (induced by source paper "recent advances in open set recognition a survey"):
  with members:
    - "open world learning and application to product classification"
    - "large scale long tailed recognition in an open world"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "bayesian semantic instance segmentation in open set world"
    - "learning a neural network based representation for open set recognition"
    - "open category detection with pac guarantees"
    - "open set domain adaptation by backpropagation"
    - "reducing network agnostophobia"

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.LG(Machine Learning)