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: "locating the source of diffusion in large scale networks"
  Abstract: How can we localize the source of diffusion in a complex network? Due to the tremendous size of many real networks---such as the Internet or the human social graph---it is usually infeasible to observe the state of all nodes in a network. We show that it is fundamentally possible to estimate the location of the source from measurements collected by sparsely-placed observers. We present a strategy that is optimal for arbitrary trees, achieving maximum probability of correct localization. We describe efficient implementations with complexity O(N^{\alpha}) , where \alpha=1 for arbitrary trees, and \alpha=3 for arbitrary graphs. In the context of several case studies, we determine how localization accuracy is affected by various system parameters, including the structure of the network, the density of observers, and the number of observed cascades.

Connected through hyperedge HE-1 (induced by source paper "noisy source location on a line"):
  with members:
    - "an adaptivity hierarchy theorem for property testing"
    - "deterministic and probabilistic binary search in graphs"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-2 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"

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"
    - "hiding the rumor source"
    - "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"

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"
    - "spy vs spy rumor source obfuscation"
    - "a robust information source estimator with sparse observations"

Connected through hyperedge HE-6 (induced by source paper "blind identification of invertible graph filters with multiple sparse inputs"):
  with members:
    - "blind identification of graph filters"
    - "network infusion to infer information sources in networks"
    - "source localization on graphs via l1 recovery and spectral graph theory"
    - "self calibration and biconvex compressive sensing"
    - "discrete signal processing on graphs"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-7 (induced by source paper "simulating sir processes on networks using weighted shortest paths"):
  with members:
    - "measuring the potential of individual airports for pandemic spread over the world airline network"
    - "identification of patient zero in static and temporal networks robustness and limitations"
    - "fastsir algorithm a fast algorithm for the simulation of the epidemic spread in large networks by using the susceptible infected recovered compartment model"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-8 (induced by source paper "locating the source in real world diffusion network"):
  with members:
    - "catch em all locating multiple diffusion sources in networks with partial observations"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion 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.SI(Social and Information Networks)