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: "an exploration of the role of principal inertia components in information theory"
  Abstract: The principal inertia components of the joint distribution of two random variables X and Y are inherently connected to how an observation of Y is statistically related to a hidden variable X. In this paper, we explore this connection within an information theoretic framework. We show that, under certain symmetry conditions, the principal inertia components play an important role in estimating one-bit functions of X, namely f(X), given an observation of Y. In particular, the principal inertia components bear an interpretation as filter coefficients in the linear transformation of p f(X)|X  into p f(X)|Y . This interpretation naturally leads to the conjecture that the mutual information between f(X) and Y is maximized when all the principal inertia components have equal value. We also study the role of the principal inertia components in the Markov chain B → X → Y → B, where B and B are binary random variables. We illustrate our results for the setting where X and Y are binary strings and Y is the result of sending X through an additive noise binary channel.

Connected through hyperedge HE-1 (induced by source paper "on the courtade kumar conjecture for certain classes of boolean functions"):
  with members:
    - "on the entropy of a noisy function"
    - "which boolean functions are most informative"

Connected through hyperedge HE-2 (induced by source paper "the maximum mutual information between the output of a discrete symmetric channel and several classes of boolean functions of its input"):
  with members:
    - "on the entropy of a noisy function"

Connected through hyperedge HE-3 (induced by source paper "on the optimal boolean function for prediction under quadratic loss"):
  with members:
    - "the most informative boolean function conjecture holds for high noise"
    - "on the entropy of a noisy function"
    - "an improved upper bound for the most informative boolean function conjecture"
    - "analysis of boolean functions"

Connected through hyperedge HE-4 (induced by source paper "the maximum mutual information between the output of a binary symmetric channel and a boolean function of its input"):
  with members:
    - "the most informative boolean function conjecture holds for high noise"
    - "on the entropy of a noisy function"
    - "an improved upper bound for the most informative boolean function conjecture"

Connected through hyperedge HE-5 (induced by source paper "maximum entropy functions approximate gacs korner for distributed compression"):
  with members:
    - "efficient coding for multi source networks using g acs k orner common information"
    - "assisted common information with an application to secure two party sampling"

Connected through hyperedge HE-6 (induced by source paper "on the duality of additivity and tensorization"):
  with members:
    - "on non interactive simulation of joint distributions"
    - "key capacity with limited one way communication for product sources"
    - "key capacity for product sources with application to stationary gaussian processes"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "evaluation of marton s inner bound for the general broadcast channel"
    - "a new data processing inequality and its applications in distributed source and channel coding"
    - "evaluation of marton s inner bound for the general broadcast channel"

Connected through hyperedge HE-7 (induced by source paper "hiding symbols and functions new metrics and constructions for information theoretic security"):
  with members:
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "rate distortion theory for secrecy systems"
    - "lists that are smaller than their parts a coding approach to tunable secrecy"
    - "universal secure network coding via rank metric codes"
    - "source coding with side information using list decoding"
    - "random linear network coding a free cipher"

Connected through hyperedge HE-8 (induced by source paper "maximal correlation secrecy"):
  with members:
    - "hiding symbols and functions new metrics and constructions for information theoretic security"
    - "on maximal correlation mutual information and data privacy"
    - "analysis of boolean functions"
    - "bounds on inference"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "lists that are smaller than their parts a coding approach to tunable secrecy"
    - "invertible extractors and wiretap protocols"

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)