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: "bounds on the error probability of raptor codes"
  Abstract: In this paper q-ary Raptor codes under ML decoding are considered. An upper bound on the probability of decoding failure is derived using the weight enumerator of the outer code, or its expected weight enumerator if the outer code is drawn randomly from some ensemble of codes. The bound is shown to be tight by means of simulations. This bound provides a new insight into Raptor codes since it shows how Raptor codes can be analyzed similarly to a classical fixed-rate serial concatenation.

Connected through hyperedge HE-1 (induced by source paper "bounds on the error probability of raptor codes under maximum likelihood decoding"):
  with members:
    - "fountain codes under maximum likelihood decoding"
    - "distance spectrum of fixed rate raptor codes with linear random precoders"
    - "on the weight distribution of fixed rate raptor codes"
    - "inactivation decoding analysis for lt codes"
    - "lt code design for inactivation decoding"

Connected through hyperedge HE-2 (induced by source paper "inactivation decoding of lt and raptor codes analysis and code design"):
  with members:
    - "distance spectrum of fixed rate raptor codes with linear random precoders"
    - "inactivation decoding analysis for lt codes"
    - "lt code design for inactivation decoding"
    - "distributed raptor coding for erasure channels partially and fully coded cooperation"
    - "analysis of the second moment of the lt decoder"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "new model for rigorous analysis of lt codes"

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)