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: "birdnest bayesian inference for ratings fraud detection"
  Abstract: Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake reviews are often detected based on several signs, including 1) they occur in short bursts of time; 2) fraudulent user accounts have skewed rating distributions. However, these may both be true in any given dataset. Hence, in this paper, we propose an approach for detecting fraudulent reviews which combines these 2 approaches in a principled manner, allowing successful detection even when one of these signs is not present. To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior. Based on our model we formulate a likelihood-based suspiciousness metric, Normalized Expected Surprise Total (NEST). We propose a linear-time algorithm for performing Bayesian inference using our model and computing the metric. Experiments on real data show that BIRDNEST successfully spots review fraud in large, real-world graphs: the 50 most suspicious users of the Flipkart platform flagged by our algorithm were investigated and all identified as fraudulent by domain experts at Flipkart.

Connected through hyperedge HE-1 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "certifiable robustness to graph perturbations"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial robustness of similarity based link prediction"
    - "adversarial defense framework for graph neural network"
    - "simplifying graph convolutional networks"
    - "learning graph neural networks with noisy labels"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-3 (induced by source paper "certifiable robustness and robust training for graph convolutional networks"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "provable robustness of relu networks via maximization of linear regions"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "neural message passing for quantum chemistry"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"

Connected through hyperedge HE-4 (induced by source paper "detection of review abuse via semi supervised binary multi target tensor decomposition"):
  with members:
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "temporal opinion spam detection by multivariate indicative signals"
    - "on the expressive power of deep learning a tensor analysis"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-5 (induced by source paper "fake news a survey of research detection methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "linknbed multi graph representation learning with entity linkage"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "belittling the source trustworthiness indicators to obfuscate fake news on the web"
    - "the spread of low credibility content by social bots"
    - "ti cnn convolutional neural networks for fake news detection"
    - "clickbait detection in tweets using self attentive network"

Connected through hyperedge HE-6 (induced by source paper "catching loosely synchronized behavior in face of camouflage"):
  with members:
    - "fairjudge trustworthy user prediction in rating platforms"
    - "towards detecting compromised accounts on social networks"
    - "edgecentric anomaly detection in edge attributed networks"
    - "spotting suspicious link behavior with fbox an adversarial perspective"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "community detection via semi synchronous label propagation algorithms"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-7 (induced by source paper "search rank fraud de anonymization in online systems"):
  with members:
    - "fairplay fraud and malware detection in google play"
    - "deepwalk online learning of social representations"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-8 (induced by source paper "designing informative rating systems evidence from an online labor market"):
  with members:
    - "designing optimal binary rating systems"
    - "matching while learning"

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)