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: "working memory networks augmenting memory networks with a relational reasoning module"
  Abstract: During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that could allow, for instance, relational reasoning. Relation Networks (RNs), on the other hand, have shown outstanding results in relational reasoning tasks. Unfortunately, their computational cost grows quadratically with the number of memories, something prohibitive for larger problems. To solve these issues, we introduce the Working Memory Network, a MemNN architecture with a novel working memory storage and reasoning module. Our model retains the relational reasoning abilities of the RN while reducing its computational complexity from quadratic to linear. We tested our model on the text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than 0.5%. Moreover, a simple ensemble of two of our models solves all 20 tasks in the joint version of the benchmark.

Connected through hyperedge HE-1 (induced by source paper "a multi level neural network for implicit causality detection in web texts"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "a structured self attentive sentence embedding"
    - "gaussian error linear units gelus"

Connected through hyperedge HE-2 (induced by source paper "relational recurrent neural networks"):
  with members:
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "non local recurrent network for image restoration"
    - "non local neural networks"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "multi layer relation networks"):
  with members:
    - "finding remo related memory object a simple neural architecture for text based reasoning"
    - "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"

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)