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: "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.

Connected through hyperedge HE-1 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator"):
  with members:
    - "computing the feedback capacity of finite state channels using reinforcement learning"
    - "conditional mutual information neural estimator"
    - "deep learning for channel coding via neural mutual information estimation"
    - "neural entropic estimation a faster path to mutual information estimation"
    - "itene intrinsic transfer entropy neural estimator"
    - "mine mutual information neural estimation"
    - "estimating the directed information and testing for causality"
    - "universal estimation of directed information"

Connected through hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-3 (induced by source paper "itene intrinsic transfer entropy neural estimator"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "ccmi classifier based conditional mutual information estimation"
    - "a brief introduction to machine learning for engineers"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "universal estimation of directed information"

Connected through hyperedge HE-4 (induced by source paper "measuring sample path causal influences with relative entropy"):
  with members:
    - "on the bias of directed information estimators"
    - "a sample path measure of causal influence"
    - "consistent tomography under partial observations over adaptive networks"
    - "k nn estimation of directed information"
    - "estimating the directed information and testing for causality"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "strong completeness and faithfulness in bayesian networks"
    - "causal networks semantics and expressiveness"

Connected through hyperedge HE-5 (induced by source paper "youla coding and computation of gaussian feedback capacity"):
  with members:
    - "control approach to computing the feedback capacity for stationary finite dimensional gaussian channels"
    - "upper bound on the capacity of gaussian channels with noisy feedback"
    - "bounds on the achievable rate of noisy feedback gaussian channels under linear feedback coding scheme"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "optimal feedback communication via posterior matching"
    - "convergence of fundamental limitations in feedback communication estimation and feedback control over gaussian channels"
    - "on the feedback capacity of power constrained gaussian noise channels with memory"
    - "a coding theorem for a class of stationary channels with feedback"

Connected through hyperedge HE-6 (induced by source paper "cheeger inequalities for submodular transformations"):
  with members:
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "polynomial time algorithms for submodular laplacian systems"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "inhomogoenous hypergraph clustering with applications"
    - "spectral properties of hypergraph laplacian and approximation algorithms"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "heat kernel based community detection"
    - "provable submodular minimization using wolfe s algorithm"

Connected through hyperedge HE-7 (induced by source paper "analogy between gambling and measurement based work extraction"):
  with members:
    - "extension of the blahut arimoto algorithm for maximizing directed information"
    - "finite state channels with time invariant deterministic feedback"

Connected through hyperedge HE-8 (induced by source paper "estimating the directed information and testing for causality"):
  with members:
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "information networks with in block memory"
    - "universal estimation of directed information"
    - "detection and isolation of link failures under the agreement protocol"
    - "the relation between granger causality and directed information theory a review"
    - "directed information causal estimation and communication in continuous time"
    - "feedback capacity of the compound channel"
    - "the capacity region of the degraded finite state broadcast channel"

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)