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: "the embedding capacity of information flows under renewal traffic"
  Abstract: Given two independent point processes and a certain rule for matching points between them, what is the fraction of matched points over infinitely long streams? In many application contexts, e.g., secure networking, a meaningful matching rule is that of a maximum causal delay, and the problem is related to embedding a flow of packets in cover traffic such that no timing analysis can detect it. We study the best undetectable embedding policy and the corresponding maximum flow rate-that we call the embedding capacity-under the assumption that the cover traffic can be modeled as an arbitrary renewal process. We find that computing the embedding capacity requires the inversion of a very structured linear system that, for a broad range of renewal models encountered in practice, admits a fully analytical expression in terms of the renewal function of the processes. This result enables us to explore the properties of the embedding capacity, obtaining closed-form solutions for selected distribution families and a suite of sufficient conditions on the capacity ordering. We test our solution on real network traces, which shows a remarkable match for tight delay constraints. A gap between the predicted and the actual embedding capacities appears for looser constraints, and further investigation reveals that it is caused by inaccuracy of the renewal traffic model rather than of the solution itself.

Connected through hyperedge HE-1 (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-2 (induced by source paper "consistent tomography under partial observations over adaptive networks"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "graph reconstruction from the observation of diffused signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "signals on graphs uncertainty principle and sampling"
    - "information diffusion of topic propagation in social media"

Connected through hyperedge HE-3 (induced by source paper "ddos attacks with randomized traffic innovation botnet identification challenges and strategies"):
  with members:
    - "distributed detection in tree networks byzantines and mitigation techniques"
    - "estimating traffic and anomaly maps via network tomography"
    - "binary hypothesis testing game with training data"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "mitigation of random query string dos via gossip"
    - "anonymous networking amidst eavesdroppers"

Connected through hyperedge HE-4 (induced by source paper "limits of reliable communication with low probability of detection on awgn channels"):
  with members:
    - "reliable deniable communication hiding messages in noise"

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.IT(Information Theory)