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: "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
  Abstract: Stock trend prediction plays a critical role in seeking maximized profit from stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of stock market. Exploding information on Internet together with advancing development of natural language processing and text mining techniques have enable investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness and comprehensiveness of online content related to stock market varies drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "exploring interpretable lstm neural networks over multi variable data"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "deep multi output forecasting learning to accurately predict blood glucose trajectories"
    - "methods for interpreting and understanding deep neural networks"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-2 (induced by source paper "financial time series forecasting with deep learning a systematic literature review 2005 2019"):
  with members:
    - "financial trading model with stock bar chart image time series with deep convolutional neural networks"
    - "forecasting economics and financial time series arima vs lstm"
    - "sentiment predictability for stocks"
    - "lstm a search space odyssey"
    - "relational autoencoder for feature extraction"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "searching for activation functions"
    - "improving stock market prediction via heterogeneous information fusion"

Connected through hyperedge HE-3 (induced by source paper "wattnet learning to trade fx via hierarchical spatio temporal representation of highly multivariate time series"):
  with members:
    - "model based deep reinforcement learning for dynamic portfolio optimization"
    - "relational recurrent neural networks"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "recurrent world models facilitate policy evolution"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "a simple neural attentive meta learner"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "temporal relational ranking for stock prediction"):
  with members:
    - "neural collaborative filtering"
    - "a survey on social media anomaly detection"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "unsupervised learning of video representations using lstms"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a deep learning framework for pricing financial instruments"):
  with members:
    - "adaptive reduced rank regression"
    - "attention is all you need"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "bidirectional lstm crf models for sequence tagging"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "stock market prediction from wsj text mining via sparse matrix factorization"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "group extract and aggregate summarizing a large amount of finance news for forex movement prediction"):
  with members:
    - "fine tune bert for extractive summarization"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "predict forex trend via convolutional neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "natural language inference by tree based convolution and heuristic matching"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "exploring graph neural networks for stock market predictions with rolling window analysis"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "scalable graph learning for anti money laundering a first look"
    - "predicting stock market movements using network science an information theoretic approach"
    - "limits of end to end learning"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "incorporating fine grained events in stock movement prediction"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "natural language inference by tree based convolution and heuristic matching"
    - "adam a method for stochastic optimization"
    - "speech recognition with deep recurrent neural 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.SI(Social and Information Networks)