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: "spectrum based deep neural networks for fraud detection"
  Abstract: In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection.

Connected through hyperedge HE-1 (induced by source paper "fraud detection in networks state of the art"):
  with members:
    - "unsupervised dictionary learning for anomaly detection"
    - "spotting collective behaviour of online frauds in customer reviews"
    - "deep learning for anomaly detection a survey"
    - "midas microcluster based detector of anomalies in edge streams"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-2 (induced by source paper "the role of bot squads in the political propaganda on twitter"):
  with members:
    - "better safe than sorry an adversarial approach to improve social bot detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "polarization of the vaccination debate on facebook"
    - "the spread of low credibility content by social bots"
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "holoscope topology and spike aware fraud detection"

Connected through hyperedge HE-3 (induced by source paper "better safe than sorry an adversarial approach to improve social bot detection"):
  with members:
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "hardening classifiers against evasion the good the bad and the ugly"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botivist calling volunteers to action using online bots"
    - "dna inspired online behavioral modeling and its application to spambot detection"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-4 (induced by source paper "one class adversarial nets for fraud detection"):
  with members:
    - "false information on web and social media a survey"
    - "good semi supervised learning that requires a bad gan"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "an army of me sockpuppets in online discussion communities"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "wikipedia vandal early detection from user behavior to user embedding"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "energy based generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "safe a neural survival analysis model for fraud early detection"):
  with members:
    - "a recurrent neural network survival model predicting web user return time"
    - "false information on web and social media a survey"
    - "deep learning for patient specific kidney graft survival analysis"
    - "machine learning for survival analysis a survey"
    - "wikipedia vandal early detection from user behavior to user embedding"
    - "vews a wikipedia vandal early warning system"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "wikipedia vandal early detection from user behavior to user embedding"):
  with members:
    - "sne signed network embedding"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "vews a wikipedia vandal early warning system"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "on spectral analysis of directed signed graphs"):
  with members:
    - "sne signed network embedding"
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "revisiting semi supervised learning with graph embeddings"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "on using very large target vocabulary for neural machine translation"

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.CR(Cryptography and Security)