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 awgn red alert problem"
  Abstract: Consider the following unequal error protection scenario. One special message, dubbed the “red alert” message, is required to have an extremely small probability of missed detection. The remainder of the messages must keep their average probability of error and probability of false alarm below a certain threshold. The goal then is to design a codebook that maximizes the error exponent of the red alert message while ensuring that the average probability of error and probability of false alarm go to zero as the blocklength goes to infinity. This red alert exponent has previously been characterized for discrete memoryless channels. This paper completely characterizes the optimal red alert exponent for additive white Gaussian noise channels with block power constraints.

Connected through hyperedge HE-1 (induced by source paper "hypothesis testing under mutual information privacy constraints in the high privacy regime"):
  with members:
    - "privacy aware mmse estimation"
    - "hypothesis testing in the high privacy limit"
    - "f divergence inequalities"
    - "on maximal correlation mutual information and data privacy"
    - "euclidean information theory of networks"
    - "extremal mechanisms for local differential privacy"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "bounds on inference"

Connected through hyperedge HE-2 (induced by source paper "unequal message protection asymptotic and non asymptotic tradeoffs"):
  with members:
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "random access and source channel coding error exponents for multiple access channels"
    - "the third order term in the normal approximation for singular channels"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"
    - "moderate deviations in channel coding"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "the dispersion of joint source channel coding"
    - "unequal error protection an information theoretic perspective"

Connected through hyperedge HE-3 (induced by source paper "on interference networks over finite fields"):
  with members:
    - "two hop interference channels impact of linear schemes"
    - "real interference alignment exploiting the potential of single antenna systems"
    - "two hop interference channels impact of linear time varying schemes"
    - "on the capacity of the finite field counterparts of wireless interference networks"
    - "structured lattice codes for some two user gaussian networks with cognition coordination and two hops"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "wireless network information flow a deterministic approach"
    - "integer forcing linear receivers"

Connected through hyperedge HE-4 (induced by source paper "bit wise unequal error protection for variable length block codes with feedback"):
  with members:
    - "errors and erasures decoding for block codes with feedback"
    - "unequal error protection an information theoretic perspective"
    - "a simple converse of burnashev s reliability function"

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)