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: "convex optimization based state estimation against sparse integrity attacks"
  Abstract: We consider the problem of robust estimation in the presence of integrity attacks. There are m sensors monitoring the state and p of them are under attack. The malicious measurements collected by the compromised sensors can be manipulated arbitrarily by the attacker. The classical estimators such as the least squares estimator may not provide a reliable estimate under the so-called (p,m)-sparse attack. In this work, we are not restricting our efforts in studying whether any specific estimator is resilient to the attack or not, but instead we aim to present some generic sufficient and necessary conditions for robustness by considering a general class of convex optimization based estimators. The sufficient and necessary conditions are shown to be tight, with a trivial gap.

Connected through hyperedge HE-1 (induced by source paper "secure distributed filtering for unstable dynamics under compromised observations"):
  with members:
    - "secure state estimation with byzantine sensors a probabilistic approach"
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "detection and mitigation of biasing attacks on distributed estimation networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"

Connected through hyperedge HE-2 (induced by source paper "an optimization framework for resilient batch estimation in cyber physical systems"):
  with members:
    - "resilient state estimation for discrete time linear systems"
    - "secure state estimation with byzantine sensors a probabilistic approach"
    - "on a class of optimization based robust estimators"

Connected through hyperedge HE-3 (induced by source paper "resilient state estimation for discrete time linear systems"):
  with members:
    - "on a class of optimization based robust estimators"

Connected through hyperedge HE-4 (induced by source paper "secure state estimation with byzantine sensors a probabilistic approach"):
  with members:
    - "on redundant observability from security index to attack detection and resilient state estimation"

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)