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: "hiding the rumor source"
  Abstract: Anonymous social media platforms like Secret, Yik Yak, and Whisper have emerged as important tools for sharing ideas without the fear of judgment. Such anonymous platforms are also important in nations under authoritarian rule, where freedom of expression and the personal safety of message authors may depend on anonymity. Whether for fear of judgment or retribution, it is sometimes crucial to hide the identities of users who post sensitive messages. In this paper, we consider a global adversary who wishes to identify the author of a message; it observes either a snapshot of the spread of a message at a certain time, sampled timestamp metadata, or both. Recent advances in rumor source detection show that existing messaging protocols are vulnerable against such an adversary. We introduce a novel messaging protocol, which we call adaptive diffusion, and show that under the snapshot adversarial model, adaptive diffusion spreads content fast and achieves perfect obfuscation of the source when the underlying contact network is an infinite regular tree. That is, all users with the message are nearly equally likely to have been the origin of the message. When the contact network is an irregular tree, we characterize the probability of maximum likelihood detection by proving a concentration result over Galton-Watson trees. Experiments on a sampled Facebook network demonstrate that adaptive diffusion effectively hides the location of the source even when the graph is finite, irregular and has cycles.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of graphs from epidemic cascades"):
  with members:
    - "learning graphs from noisy epidemic cascades"
    - "meta learning with relational information for short sequences"
    - "em converges for a mixture of many linear regressions"
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from noisy epidemic cascades"):
  with members:
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "correlated cascades compete or cooperate"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "spy vs spy rumor source obfuscation"
    - "an efficient curing policy for epidemics on graphs"
    - "can cascades be predicted"

Connected through hyperedge HE-3 (induced by source paper "who started this rumor quantifying the natural differential privacy guarantees of gossip protocols"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "stochastic gradient push for distributed deep learning"
    - "distributed differential privacy via shuffling"
    - "personalized and private peer to peer machine learning"
    - "decentralized collaborative learning of personalized models over networks"
    - "how to discreetly spread a rumor in a crowd"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (induced by source paper "information source detection with limited time knowledge"):
  with members:
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "estimating infection sources in networks using partial timestamps"
    - "catch em all locating multiple diffusion sources in networks with partial observations"
    - "multiple infection sources identification with provable guarantees"
    - "how to identify an infection source with limited observations"
    - "a robust information source estimator with sparse observations"
    - "information source detection in the sir model a sample path based approach"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-5 (induced by source paper "disentangling mixtures of epidemics on graphs"):
  with members:
    - "learning graphs from noisy epidemic cascades"
    - "fake news mitigation via point process based intervention"
    - "a lower bound on the performance of dynamic curing policies for epidemics on graphs"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "an efficient curing policy for epidemics on graphs"

Connected through hyperedge HE-6 (induced by source paper "an operational approach to information leakage"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "technical privacy metrics a systematic survey"
    - "measuring secrecy by the probability of a successful guess"
    - "principal inertia components and applications"
    - "privacy utility tradeoffs under constrained data release mechanisms"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "a large deviations approach to secure lossy compression"
    - "mitigating timing side channel in shared schedulers"

Connected through hyperedge HE-7 (induced by source paper "effects of degree correlations in interdependent security good or bad"):
  with members:
    - "internalization of externalities in interdependent security large network cases"
    - "interdependent security with strategic agents and cascades of infection"
    - "a local mean field analysis of security investments in 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.CR(Cryptography and Security)