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: "scalable anomaly ranking of attributed neighborhoods"
  Abstract: Given a graph with node attributes, what neighborhoods are anomalous? To answer this question, one needs a quality score that utilizes both structure and attributes. Popular existing measures either quantify the structure only and ignore the attributes (e.g., conductance), or only consider the connectedness of the nodes inside the neighborhood and ignore the cross-edges at the boundary (e.g., density). #R##N#In this work we propose normality, a new quality measure for attributed neighborhoods. Normality utilizes structure and attributes together to quantify both internal consistency and external separability. It exhibits two key advantages over other measures: (1) It allows many boundary-edges as long as they can be "exonerated"; i.e., either (i) are expected under a null model, and/or (ii) the boundary nodes do not exhibit the subset of attributes shared by the neighborhood members. Existing measures, in contrast, penalize boundary edges irrespectively. (2) Normality can be efficiently maximized to automatically infer the shared attribute subspace (and respective weights) that characterize a neighborhood. This efficient optimization allows us to process graphs with millions of attributes. #R##N#We capitalize on our measure to present a novel approach for Anomaly Mining of Entity Neighborhoods (AMEN). Experiments on real-world attributed graphs illustrate the effectiveness of our measure at anomaly detection, outperforming popular approaches including conductance, density, OddBall, and SODA. In addition to anomaly detection, our qualitative analysis demonstrates the utility of normality as a powerful tool to contrast the correlation between structure and attributes across different graphs.

Connected through hyperedge HE-1 (induced by source paper "anomalydae dual autoencoder for anomaly detection on attributed networks"):
  with members:
    - "spamhmm sparse mixture of hidden markov models for graph connected entities"
    - "attributed social network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "semi supervised classification with graph convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "graphsac detecting anomalies in large scale graphs"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "learning structural node embeddings via diffusion wavelets"
    - "deep defense training dnns with improved adversarial robustness"
    - "towards robust detection of adversarial examples"
    - "semi supervised classification with graph convolutional networks"
    - "heat kernel based community detection"
    - "pagerank beyond the web"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-4 (induced by source paper "a generic framework for interesting subspace cluster detection in multi attributed networks"):
  with members:
    - "convexification of learning from constraints"
    - "outlier detection from network data with subnetwork interpretation"
    - "approximation algorithms for model based compressive sensing"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-5 (induced by source paper "distributed representation of subgraphs"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"
    - "line large scale information network embedding"
    - "distributed representations of words and phrases and their compositionality"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "contextual outlier interpretation"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"
    - "robust contextual outlier detection where context meets sparsity"
    - "identifying the signs of fraudulent accounts using data mining techniques"

Connected through hyperedge HE-7 (induced by source paper "hitfraud a broad learning approach for collective fraud detection in heterogeneous information networks"):
  with members:
    - "spotting suspicious link behavior with fbox an adversarial perspective"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-8 (induced by source paper "ties that bind characterizing classes by attributes and social ties"):
  with members:
    - "combinatorial auctions with decreasing marginal utilities"

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)