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: "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
  Abstract: Classifying products into categories precisely and efficiently is a major challenge in modern e-commerce. The high traffic of new products uploaded daily and the dynamic nature of the categories raise the need for machine learning models that can reduce the cost and time of human editors. In this paper, we propose a decision level fusion approach for multi-modal product classification using text and image inputs. We train input specific state-of-the-art deep neural networks for each input source, show the potential of forging them together into a multi-modal architecture and train a novel policy network that learns to choose between them. Finally, we demonstrate that our multi-modal network improves the top-1 accuracy % over both networks on a real-world large-scale product classification dataset that we collected fromWalmart.com. While we focus on image-text fusion that characterizes e-commerce domains, our algorithms can be easily applied to other modalities such as audio, video, physical sensors, etc.

Connected through hyperedge HE-1 (induced by source paper "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"):
  with members:
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "the uncertainty bellman equation and exploration"
    - "a tutorial on thompson sampling"
    - "curiosity driven exploration by self supervised prediction"
    - "graying the black box understanding dqns"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-2 (induced by source paper "dynamic deep multi modal fusion for image privacy prediction"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "places an image database for deep scene understanding"
    - "convolutional two stream network fusion for video action recognition"
    - "object scene convolutional neural networks for event recognition in images"
    - "images don t lie transferring deep visual semantic features to large scale multimodal learning to rank"
    - "meta des"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "multi label product categorization using multi modal fusion models"):
  with members:
    - "attending to mathematical language with transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "image based recommendations on styles and substitutes"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "large scale product categorization using structured and unstructured attributes"):
  with members:
    - "don t classify translate multi level e commerce product categorization via machine translation"
    - "entity embeddings of categorical variables"
    - "product classification in e commerce using distributional semantics"
    - "cyclical learning rates for training neural networks"
    - "towards universal paraphrastic sentence embeddings"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-6 (induced by source paper "a visual technique to analyze flow of information in a machine learning system"):
  with members:
    - "activis visual exploration of industry scale deep neural network models"

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)