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: "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
  Abstract: CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent methods, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "a unified system for aggression identification in english code mixed and uni lingual texts"):
  with members:
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "towards generating long and coherent text with multi level latent variable models"
    - "mattnet modular attention network for referring expression comprehension"
    - "universal language model fine tuning for text classification"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification"):
  with members:
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "towards generating long and coherent text with multi level latent variable models"
    - "mattnet modular attention network for referring expression comprehension"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"
    - "advances in pre training distributed word representations"
    - "comparative study of cnn and rnn for natural language processing"

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.IR(Information Retrieval)