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: "identifying the signs of fraudulent accounts using data mining techniques"
  Abstract: In today's technological society there are various new means to commit fraud due to the advancement of media and communication networks. One typical fraud is the ATM phone scams. The commonality of ATM phone scams is basically to attract victims to use financial institutions or ATMs to transfer their money into fraudulent accounts. Regardless of the types of fraud used, fraudsters can only collect victims' money through fraudulent accounts. Therefore, it is very important to identify the signs of such fraudulent accounts and to detect fraudulent accounts based on these signs, in order to reduce victims' losses. This study applied Bayesian Classification and Association Rule to identify the signs of fraudulent accounts and the patterns of fraudulent transactions. Detection rules were developed based on the identified signs and applied to the design of a fraudulent account detection system. Empirical verification supported that this fraudulent account detection system can successfully identify fraudulent accounts in early stages and is able to provide reference for financial institutions.

Connected through hyperedge HE-1 (induced by source paper "memory augmented generative adversarial networks for anomaly detection"):
  with members:
    - "regularized cycle consistent generative adversarial network for anomaly detection"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "anomaly detection with multiple hypotheses predictions"
    - "adversarially learned anomaly detection"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-2 (induced by source paper "adversarial learning of deepfakes in accounting"):
  with members:
    - "detection of accounting anomalies in the latent space using adversarial autoencoder neural networks"
    - "text based editing of talking head video"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "voice conversion with conditional samplernn"
    - "adversarial text generation via feature mover s distance"
    - "adversarial attacks and defences a survey"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "everybody dance now"

Connected through hyperedge HE-3 (induced by source paper "sequence embeddings help to identify fraudulent cases in healthcare insurance"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "anonymous walk embeddings"
    - "on the dimensionality of word embedding"
    - "representation learning on graphs methods and applications"
    - "practical black box attacks against machine learning"
    - "xgboost a scalable tree boosting system"
    - "entity embedding based anomaly detection for heterogeneous categorical events"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "tapestry a blockchain based service for trusted interaction online"):
  with members:
    - "ethical dimensions of visualization research"
    - "composite behavioral modeling for identity theft detection in online social networks"
    - "group anomaly detection using deep generative models"
    - "a first look at identity management schemes on the blockchain"
    - "deep image retrieval learning global representations for image search"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "facenet a unified embedding for face recognition and clustering"
    - "a primer on neural network models for natural language processing"

Connected through hyperedge HE-5 (induced by source paper "titant online real time transaction fraud detection in ant financial"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "node2vec scalable feature learning for networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "an encoder decoder based approach for anomaly detection with application in additive manufacturing"):
  with members:
    - "detecting and diagnosing incipient building faults using uncertainty information from deep neural networks"
    - "a one class support vector machine calibration method for time series change point detection"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "detecting problematic transactions in a consumer to consumer e commerce network"):
  with members:
    - "holoscope topology and spike aware fraud detection"
    - "anomaly detection in online social networks"
    - "machine learning for e mail spam filtering review techniques and trends"
    - "detecting criminal organizations in mobile phone networks"
    - "graph based anomaly detection and description a survey"
    - "an expert system for detecting automobile insurance fraud using social network analysis"

Connected through hyperedge HE-8 (induced by source paper "automated analysis of femoral artery calcification using machine learning techniques"):
  with members:
    - "modelling of sickle cell anemia patients response to hydroxyurea using artificial neural networks"
    - "end to end learning for self driving cars"
    - "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.AI(Artificial Intelligence)