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 with memory embeddings"
  Abstract: Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Latent variable models are well suited to deal with the high dimensionality and sparsity of typical knowledge graphs. In recent publications the embedding models were extended to also consider time evolutions, time patterns and subsymbolic representations. In this paper we map embedding models, which were developed purely as solutions to technical problems for modelling temporal knowledge graphs, to various cognitive memory functions, in particular to semantic and concept memory, episodic memory, sensory memory, short-term memory, and working memory. We discuss learning, query answering, the path from sensory input to semantic decoding, and the relationship between episodic memory and semantic memory. We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.

Connected through hyperedge HE-1 (induced by source paper "the tensor brain semantic decoding for perception and memory"):
  with members:
    - "reasoning on knowledge graphs with debate dynamics"
    - "improving visual relation detection using depth maps"
    - "learning by abstraction the neural state machine"
    - "context aware zero shot recognition"
    - "embedding models for episodic knowledge graphs"
    - "improving information extraction from images with learned semantic models"
    - "graph r cnn for scene graph generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "efficient dialog policy learning via positive memory retention"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "learning goal oriented visual dialog via tempered policy gradient"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-3 (induced by source paper "improving visual relationship detection using semantic modeling of scene descriptions"):
  with members:
    - "visual relationship detection with language priors"
    - "complex embeddings for simple link prediction"
    - "fast r cnn"
    - "neural module networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-4 (induced by source paper "improving information extraction from images with learned semantic models"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "the tensor memory hypothesis"
    - "visual translation embedding network for visual relation detection"
    - "visual relationship detection with language priors"
    - "very deep convolutional networks for large scale image recognition"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "the tensor memory hypothesis"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "predictive clinical decision support system with rnn encoding and tensor decoding"
    - "dense associative memory for pattern recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "predicting the co evolution of event and knowledge graphs"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-6 (induced by source paper "predicting the co evolution of event and knowledge graphs"):
  with members:
    - "towards a new science of a clinical data intelligence"
    - "representation learning a review and new perspectives"

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.AI(Artificial Intelligence)