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: "higru hierarchical gated recurrent units for utterance level emotion recognition"
  Abstract: In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to capture the contexts of utterance-level embeddings. Moreover, we promote the framework to two variants, HiGRU with individual features fusion (HiGRU-f) and HiGRU with self-attention and features fusion (HiGRU-sf), so that the word/utterance-level individual inputs and the long-range contextual information can be sufficiently utilized. Experiments on three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate that our proposed HiGRU models attain at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset, respectively. Particularly, by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at least 3.8% improvement over the state-of-the-art conversational memory network (CMN) with the trimodal features of text, video, and audio.

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:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "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:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "hierarchical transformer network for utterance level emotion recognition"):
  with members:
    - "pt code pre trained context dependent encoder for utterance level emotion recognition"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "multi task learning with auxiliary speaker identification for conversational emotion recognition"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "multi task learning with language modeling for question generation"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "adversarial multi task learning for text classification"

Connected through hyperedge HE-5 (induced by source paper "multi task learning network for emotion recognition in conversation"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "different absorption from the same sharing sifted multi task learning for fake news detection"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "multi task learning with language modeling for question generation"
    - "deep contextualized word representations"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "emotion recognition in conversations with transfer learning from generative conversation modeling"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "transfer learning for sequence labeling using source model and target data"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"

Connected through hyperedge HE-7 (induced by source paper "real time emotion recognition via attention gated hierarchical memory network"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "a deep neural framework for contextual affect detection"):
  with members:
    - "emotionlines an emotion corpus of multi party conversations"
    - "deep contextualized word representations"
    - "emotionx dlc self attentive bilstm for detecting sequential emotions in dialogue"
    - "attention is all you need"
    - "recurrent neural network for text classification with multi task learning"
    - "gated feedback recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)