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: "spy vs spy rumor source obfuscation"
  Abstract: Anonymous messaging platforms, such as Secret and Whisper, have emerged as important social media for sharing one's thoughts without the fear of being judged by friends, family, or the public. Further, such anonymous platforms are crucial in nations with authoritarian governments; the right to free expression and sometimes the personal safety of the author of the message depend on anonymity. Whether for fear of judgment or personal endangerment, it is crucial to keep anonymous the identity of the user who initially posted a sensitive message. In this paper, we consider an adversary who observes a snapshot of the spread of a message at a certain time. Recent advances in rumor source detection shows that the existing messaging protocols are vulnerable against such an adversary. We introduce a novel messaging protocol, which we call adaptive diffusion, and show that it spreads the messages fast and achieves a perfect obfuscation of the source when the underlying contact network is an infinite regular tree: all users with the message are nearly equally likely to have been the origin of the message. Experiments on a sampled Facebook network show that it effectively hides the location of the source even when the graph is finite, irregular and has cycles. We further consider a stronger adversarial model where a subset of colluding users track the reception of messages. We show that the adaptive diffusion provides a strong protection of the anonymity of the source even under this scenario.

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"
    - "hiding the rumor source"
    - "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"
    - "an efficient curing policy for epidemics on graphs"
    - "can cascades be predicted"

Connected through hyperedge HE-3 (induced by source paper "dynamically protecting privacy under uncertainty"):
  with members:
    - "query complexity of bayesian private learning"
    - "private sequential learning"
    - "quantifying surveillance in the networked age node based intrusions and group privacy"
    - "perception games and privacy"
    - "riposte an anonymous messaging system handling millions of users"
    - "the empirical implications of privacy aware choice"
    - "privacy preserving public information for sequential games"
    - "robust adaptive routing under uncertainty"

Connected through hyperedge HE-4 (induced by source paper "query complexity of bayesian private learning"):
  with members:
    - "private sequential learning"
    - "the empirical implications of privacy aware choice"
    - "differentially private online learning"
    - "iterative constructions and private data release"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "dandelion lightweight cryptocurrency networking with formal anonymity guarantees"):
  with members:
    - "dandelion redesigning the bitcoin network for anonymity"
    - "anonymity properties of the bitcoin p2p network"
    - "hijacking bitcoin routing attacks on cryptocurrencies"
    - "hijacking bitcoin large scale network attacks on cryptocurrencies"
    - "deanonymisation of clients in bitcoin p2p network"
    - "bitcoin over tor isn t a good idea"
    - "a robust information source estimator with sparse observations"
    - "pisces anonymous communication using social networks"

Connected through hyperedge HE-6 (induced by source paper "a flexible network approach to privacy of blockchain transactions"):
  with members:
    - "dandelion redesigning the bitcoin network for anonymity"
    - "deanonymisation of clients in bitcoin p2p network"

Connected through hyperedge HE-7 (induced by source paper "estimating infection sources in networks using partial timestamps"):
  with members:
    - "optimal localization of diffusion sources in complex networks"
    - "modeling the propagation of trojan malware in online social networks"
    - "infection spreading and source identification a hide and seek game"
    - "source localization in networks trees and beyond"
    - "how to identify an infection source with limited observations"
    - "identifying infection sources and regions in large networks"
    - "a robust information source estimator with sparse observations"
    - "finding an infection source under the sis model"

Connected through hyperedge HE-8 (induced by source paper "dandelion redesigning the bitcoin network for anonymity"):
  with members:
    - "hijacking bitcoin routing attacks on cryptocurrencies"
    - "hijacking bitcoin large scale network attacks on cryptocurrencies"
    - "deanonymisation of clients in bitcoin p2p network"
    - "bitcoin over tor isn t a good idea"
    - "locating contagion sources in networks with partial timestamps"
    - "a robust information source estimator with sparse observations"
    - "locating the source of diffusion in large scale networks"
    - "max product for maximum weight matching convergence correctness and lp duality"

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)