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: "variable length coding with cost allowing non vanishing error probability"
  Abstract: We derive a general formula of the minimum achievable rate for fixed-to-variable length coding with a regular cost function by allowing the error probability up to a constant $\varepsilon$. For a fixed-to-variable length code, we call the set of source sequences that can be decoded without error the dominant set of source sequences. For any two regular cost functions, it is revealed that the dominant set of source sequences for a code attaining the minimum achievable rate with a cost function is also the dominant set for a code attaining the minimum achievable rate with the other cost function. We also give a general formula of the second-order minimum achievable rate.

Connected through hyperedge HE-1 (induced by source paper "optimum overflow thresholds in variable length source coding allowing non vanishing error probability"):
  with members:
    - "variable length compression allowing errors"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-2 (induced by source paper "variable length resolvability for mixed sources and its application to variable length source coding"):
  with members:
    - "variable length resolvability for general sources and channels"
    - "variable length compression allowing errors"

Connected through hyperedge HE-3 (induced by source paper "variable length resolvability for general sources and channels"):
  with members:
    - "strong converse and second order asymptotics of channel resolvability"
    - "an information spectrum approach to weak variable length source coding with side information"
    - "variable length compression allowing errors"
    - "second order resolvability intrinsic randomness and fixed length source coding for mixed sources information spectrum approach"

Connected through hyperedge HE-4 (induced by source paper "overflow probability of variable length codes with codeword cost"):
  with members:
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "channels with cost constraints strong converse and dispersion"
    - "second order resolvability intrinsic randomness and fixed length source coding for mixed sources information spectrum approach"
    - "on the relationships among optimal symmetric fix free codes"
    - "information spectrum approach to overflow probability of variable length codes with conditional cost function"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

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)