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: "learning concept taxonomies from multi modal data"
  Abstract: We study the problem of automatically building hypernym taxonomies from textual and visual data. Previous works in taxonomy induction generally ignore the increasingly prominent visual data, which encode important perceptual semantics. Instead, we propose a probabilistic model for taxonomy induction by jointly leveraging text and images. To avoid hand-crafted feature engineering, we design end-to-end features based on distributed representations of images and words. The model is discriminatively trained given a small set of existing ontologies and is capable of building full taxonomies from scratch for a collection of unseen conceptual label items with associated images. We evaluate our model and features on the WordNet hierarchies, where our system outperforms previous approaches by a large gap.

Connected through hyperedge HE-1 (induced by source paper "interpretable image recognition with hierarchical prototypes"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "this looks like that deep learning for interpretable image recognition"
    - "branchconnect image categorization with learned branch connections"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-2 (induced by source paper "end to end reinforcement learning for automatic taxonomy induction"):
  with members:
    - "unsupervised hypernym detection by distributional inclusion vector embedding"
    - "why we have switched from building full fledged taxonomies to simply detecting hypernymy relations"
    - "taxonomy induction using hypernym subsequences"
    - "lifted rule injection for relation embeddings"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"):
  with members:
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "inferring missing entity type instances for knowledge base completion new dataset and methods"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)