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: "finding remo related memory object a simple neural architecture for text based reasoning"
  Abstract: Memory Network based models have shown a remarkable progress on the task of relational reasoning. Recently, a simpler yet powerful neural network module called Relation Network (RN) has been introduced. Despite its architectural simplicity, the time complexity of relation network grows quadratically with data, hence limiting its application to tasks with a large-scaled memory. We introduce Related Memory Network, an end-to-end neural network architecture exploiting both memory network and relation network structures. We follow memory network's four components while each component operates similar to the relation network without taking a pair of objects. As a result, our model is as simple as RN but the computational complexity is reduced to linear time. It achieves the state-of-the-art results in jointly trained bAbI-10k story-based question answering and bAbI dialog dataset.

Connected through hyperedge HE-1 (induced by source paper "robust and scalable differentiable neural computer for question answering"):
  with members:
    - "self normalizing neural networks"
    - "reasonet learning to stop reading in machine comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "text understanding with the attention sum reader network"
    - "tracking the world state with recurrent entity networks"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-2 (induced by source paper "multi layer relation networks"):
  with members:
    - "working memory networks augmenting memory networks with a relational reasoning module"
    - "measuring abstract reasoning in neural networks"
    - "adaptive memory networks"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "relnet end to end modeling of entities relations"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-3 (induced by source paper "recurrent relational networks"):
  with members:
    - "a simple neural network module for relational reasoning"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "neural message passing for quantum chemistry"
    - "optnet differentiable optimization as a layer in neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "tracking the world state with recurrent entity networks"
    - "building machines that learn and think like people"
    - "end to end memory networks"

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)