human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "interpretations of directed information in portfolio theory data compression and hypothesis testing"):
  Abstract: We investigate the role of Massey's directed information in portfolio theory, data compression, and statistics with causality constraints. In particular, we show that directed information is an upper bound on the increment in growth rates of optimal portfolios in a stock market due to {causal} side information. This upper bound is tight for gambling in a horse race, which is an extreme case of stock markets. Directed information also characterizes the value of {causal} side information in instantaneous compression and quantifies the benefit of {causal} inference in joint compression of two stochastic processes. In hypothesis testing, directed information evaluates the best error exponent for testing whether a random process $Y$ {causally} influences another process $X$ or not. These results give a natural interpretation of directed information $I(Y^n \to X^n)$ as the amount of information that a random sequence $Y^n = (Y_1,Y_2,..., Y_n)$ {causally} provides about another random sequence $X^n = (X_1,X_2,...,X_n)$. A new measure, {\em directed lautum information}, is also introduced and interpreted in portfolio theory, data compression, and hypothesis testing.

Includes members:
  Member "directed information causal estimation and communication in continuous time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective")
  Member "feedback capacity of the compound channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback")
  Member "the capacity region of the degraded finite state broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the secrecy capacity of gaussian mimo channels with finite memory full version")
  Member "a coding theorem for a class of stationary channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "achieving the gaussian rate distortion function by prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes")
  Member "finite state channels with time invariant deterministic feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "the capacity of channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises")

Question: Please tell me which class the source paper 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)