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: "link prediction with mutual attention for text attributed networks"
  Abstract: In this extended abstract, we present an algorithm that learns a similarity measure between documents from the network topology of a structured corpus. We leverage the Scaled Dot-Product Attention, a recently proposed attention mechanism, to design a mutual attention mechanism between pairs of documents. To train its parameters, we use the network links as supervision. We provide preliminary experiment results with a citation dataset on two prediction tasks, demonstrating the capacity of our model to learn a meaningful textual similarity.

Connected through hyperedge HE-1 (induced by source paper "inductive document network embedding with topic word attention"):
  with members:
    - "global vectors for node representations"
    - "verse versatile graph embeddings from similarity measures"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "node2vec scalable feature learning for networks"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms towards network intervention"):
  with members:
    - "link prediction based on graph neural networks"
    - "mining online social data for detecting social network mental disorders"

Connected through hyperedge HE-3 (induced by source paper "water supply prediction based on initialized attention residual network"):
  with members:
    - "learning data augmentation strategies for object detection"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "random erasing data augmentation"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "language modeling with gated convolutional networks"
    - "neural machine translation in linear time"

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)