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: "anonymous networking amidst eavesdroppers"
  Abstract: The problem of security against timing based traffic analysis in wireless networks is considered in this work. An analytical measure of anonymity in eavesdropped networks is proposed using the information theoretic concept of equivocation. For a physical layer with orthogonal transmitter directed signaling, scheduling and relaying techniques are designed to maximize achievable network performance for any given level of anonymity. The network performance is measured by the achievable relay rates from the sources to destinations under latency and medium access constraints. In particular, analytical results are presented for two scenarios: #R##N#For a two-hop network with maximum anonymity, achievable rate regions for a general m x 1 relay are characterized when nodes generate independent Poisson transmission schedules. The rate regions are presented for both strict and average delay constraints on traffic flow through the relay. #R##N#For a multihop network with an arbitrary anonymity requirement, the problem of maximizing the sum-rate of flows (network throughput) is considered. A selective independent scheduling strategy is designed for this purpose, and using the analytical results for the two-hop network, the achievable throughput is characterized as a function of the anonymity level. The throughput-anonymity relation for the proposed strategy is shown to be equivalent to an information theoretic rate-distortion function.

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 "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-3 (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-4 (induced by source paper "measuring secrecy by the probability of a successful guess"):
  with members:
    - "a large deviations approach to secure lossy compression"
    - "mitigating timing side channel in shared schedulers"
    - "the henchman problem measuring secrecy by the minimum distortion in a list"
    - "rate distortion theory for secrecy systems"
    - "improved rate equivocation regions for secure cooperative communication"
    - "lossless compression with security constraints"
    - "shannon s secrecy system with informed receivers and its application to systematic coding for wiretapped channels"

Connected through hyperedge HE-5 (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"
    - "the embedding capacity of information flows under renewal traffic"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "the embedding capacity of information flows under renewal traffic"):
  with members:
    - "capacity bounds for broadcast channels with confidential messages"

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)