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 information spectrum approach to weak variable length source coding with side information"
  Abstract: This paper studies variable-length (VL) source coding of general sources with side-information. Novel one-shot coding theorems for coding with common side-information available at the encoder and the decoder and Slepian- Wolf (SW) coding (i.e., with side-information only at the decoder) are given, and then, are applied to asymptotic analyses of these coding problems. Especially, a general formula for the infimum of the coding rate asymptotically achievable by weak VL-SW coding (i.e., VL-SW coding with vanishing error probability) is derived. Further, the general formula is applied to investigating weak VL-SW coding of mixed sources. Our results derive and extend several known results on SW coding and weak VL coding, e.g., the optimal achievable rate of VL-SW coding for mixture of i.i.d. sources is given for countably infinite alphabet case with mild condition. In addition, the usefulness of the encoder side-information is investigated. Our result shows that if the encoder side-information is useless in weak VL coding then it is also useless even in the case where the error probability may be positive asymptotically.

Connected through hyperedge HE-1 (induced by source paper "variable length source dispersions differ under maximum and average error criteria"):
  with members:
    - "on the conditional smooth renyi entropy and its applications in guessing and source coding"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "variable length compression allowing errors"
    - "information spectrum approach to second order coding rate in channel coding"
    - "duality between channel capacity and rate distortion with two sided state information"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-2 (induced by source paper "variable length resolvability for general sources and channels"):
  with members:
    - "variable length coding with cost allowing non vanishing error probability"
    - "strong converse and second order asymptotics of channel resolvability"
    - "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-3 (induced by source paper "variable length coding with cost 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-4 (induced by source paper "a dichotomy of functions in distributed coding an information spectral approach"):
  with members:
    - "moderate deviations in 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)