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: "spotting suspicious link behavior with fbox an adversarial perspective"
  Abstract: How can we detect suspicious users in large online networks? Online popularity of a user or product (via follows, page-likes, etc.) can be monetized on the premise of higher ad click-through rates or increased sales. Web services and social networks which incentivize popularity thus suffer from a major problem of fake connections from link fraudsters looking to make a quick buck. Typical methods of catching this suspicious behavior use spectral techniques to spot large groups of often blatantly fraudulent (but sometimes honest) users. However, small-scale, stealthy attacks may go unnoticed due to the nature of low-rank eigenanalysis used in practice. #R##N#In this work, we take an adversarial approach to find and prove claims about the weaknesses of modern, state-of-the-art spectral methods and propose fBox, an algorithm designed to catch small-scale, stealth attacks that slip below the radar. Our algorithm has the following desirable properties: (a) it has theoretical underpinnings, (b) it is shown to be highly effective on real data and (c) it is scalable (linear on the input size). We evaluate fBox on a large, public 41.7 million node, 1.5 billion edge who-follows-whom social graph from Twitter in 2010 and with high precision identify many suspicious accounts which have persisted without suspension even to this day.

Connected through hyperedge HE-1 (induced by source paper "catching loosely synchronized behavior in face of camouflage"):
  with members:
    - "fairjudge trustworthy user prediction in rating platforms"
    - "birdnest bayesian inference for ratings fraud detection"
    - "towards detecting compromised accounts on social networks"
    - "edgecentric anomaly detection in edge attributed networks"
    - "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-2 (induced by source paper "semi supervised content based detection of misinformation via tensor embeddings"):
  with members:
    - "in search of credible news"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "fake news detection on social media a data mining perspective"

Connected through hyperedge HE-3 (induced by source paper "no place to hide catching fraudulent entities in tensors"):
  with members:
    - "distributed methods for high dimensional and large scale tensor factorization"

Connected through hyperedge HE-4 (induced by source paper "densealert incremental dense subtensor detection in tensor streams"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "densest subgraph in dynamic graph streams"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "densest subgraph in streaming and mapreduce"

Connected through hyperedge HE-5 (induced by source paper "hitfraud a broad learning approach for collective fraud detection in heterogeneous information networks"):
  with members:
    - "scalable anomaly ranking of attributed neighborhoods"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-6 (induced by source paper "the many faces of link fraud"):
  with members:
    - "what they do in shadows twitter underground follower market"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-7 (induced by source paper "graph summarization methods and applications a survey"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "by chance is not enough preserving relative density through non uniform sampling"
    - "node2vec scalable feature learning for networks"
    - "compressing graphs and indexes with recursive graph bisection"
    - "network structure inference a survey motivations methods and applications"
    - "efficient frequent directions algorithm for sparse matrices"
    - "pnp fast path ensemble method for movie design"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-8 (induced by source paper "edgecentric anomaly detection in edge attributed networks"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "birdnest bayesian inference for ratings fraud detection"
    - "vog summarizing and understanding large graphs"

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.LG(Machine Learning)