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: "typical sequences for polish alphabets"
  Abstract: The notion of typical sequences plays a key role in the theory of information. Central to the idea of typicality is that a sequence $x_1, x_2, ..., x_n$ that is $P_X$-typical should, loosely speaking, have an empirical distribution that is in some sense close to the distribution $P_X$. The two most common notions of typicality are that of strong (letter) typicality and weak (entropy) typicality. While weak typicality allows one to apply many arguments that can be made with strongly typical arguments, some arguments for strong typicality cannot be generalized to weak typicality. In this paper, we consider an alternate definition of typicality, namely one based on the weak* topology and that is applicable to Polish alphabets (which includes $\reals^n$). This notion is a generalization of strong typicality in the sense that it degenerates to strong typicality in the finite alphabet case, and can also be applied to mixed and continuous distributions. Furthermore, it is strong enough to prove a Markov lemma, and thus can be used to directly prove a more general class of results than weak typicality. As an example of this technique, we directly prove achievability for Gel'fand-Pinsker channels with input constraints for a large class of alphabets and channels without first proving a finite alphabet result and then resorting to delicate quantization arguments. While this large class does not include Gaussian distributions with power constraints, it is shown to be straightforward to recover this case by considering a sequence of truncated Gaussian distributions.

Connected through hyperedge HE-1 (induced by source paper "the likelihood encoder for lossy compression"):
  with members:
    - "joint source channel secrecy using hybrid coding"
    - "a rate distortion based secrecy system with side information at the decoders"
    - "a generalized typicality for abstract alphabets"
    - "the likelihood encoder for lossy source compression"
    - "a technique for deriving one shot achievability results in network information theory"
    - "non asymptotic output statistics of random binning and its applications"
    - "a connection between good rate distortion codes and backward dmcs"
    - "the likelihood encoder for source coding"

Connected through hyperedge HE-2 (induced by source paper "empirical processes typical sequences and coordinated actions in standard borel spaces"):
  with members:
    - "mismatched codebooks and the role of entropy coding in lossy data compression"

Connected through hyperedge HE-3 (induced by source paper "nested lattice codes for arbitrary continuous sources and channels"):
  with members:
    - "on general lattice quantization noise"
    - "lattice strategies for the dirty multiple access channel"
    - "a layered lattice coding scheme for a class of three user gaussian interference channels"
    - "distributed source coding using abelian group codes"

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)