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: "which boolean functions are most informative"
  Abstract: We introduce a simply stated conjecture regarding the maximum mutual information a Boolean function can reveal about noisy inputs. Specifically, let $X^n$ be i.i.d. Bernoulli(1/2), and let $Y^n$ be the result of passing $X^n$ through a memoryless binary symmetric channel with crossover probability $\alpha$. For any Boolean function $b:\{0,1\}^n\rightarrow \{0,1\}$, we conjecture that $I(b(X^n);Y^n)\leq 1-H(\alpha)$. While the conjecture remains open, we provide substantial evidence supporting its validity.

Connected through hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "on the courtade kumar conjecture for certain classes of boolean functions"):
  with members:
    - "on the entropy of a noisy function"
    - "an exploration of the role of principal inertia components in information theory"

Connected through hyperedge HE-3 (induced by source paper "distributed information theoretic biclustering"):
  with members:
    - "demystifying information theoretic clustering"
    - "evaluation of marton s inner bound for the general broadcast channel"
    - "multiterminal source coding under logarithmic loss"
    - "evaluation of marton s inner bound for the general broadcast channel"

Connected through hyperedge HE-4 (induced by source paper "dictator functions maximize mutual information"):
  with members:
    - "remarks on the most informative function conjecture at fixed mean"
    - "an improved upper bound for the most informative boolean function conjecture"
    - "canalizing boolean functions maximize the mutual information"

Connected through hyperedge HE-5 (induced by source paper "on non interactive simulation of joint distributions"):
  with members:
    - "exact common information"
    - "distributed channel synthesis"
    - "channel simulation via interactive communications"
    - "generating dependent random variables over networks"
    - "on extracting common random bits from correlated sources"
    - "communication requirements for generating correlated random variables"

Connected through hyperedge HE-6 (induced by source paper "an exploration of the role of principal inertia components in information theory"):
  with members:
    - "bounds on inference"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "a new data processing inequality and its applications in distributed source and channel coding"

Connected through hyperedge HE-7 (induced by source paper "bounds on inference"):
  with members:
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "a new data processing inequality and its applications in distributed source and channel coding"

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)