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: "equal image size source partitioning creating strong fano s inequalities for multi terminal discrete memoryless channels"
  Abstract: This paper introduces equal-image-size source partitioning, a new tool for analyzing channel and joint source-channel coding in a multi-terminal discrete memoryless channel environment. Equal-image-size source partitioning divides the source (combination of messages and codewords) into a sub-exponential number of subsets. Over each of these subsets, the exponential orders of the minimum image sizes of most messages are roughly equal to the same entropy term. This property gives us the strength of minimum image sizes and the flexibility of entropy terms. Using the method of equal-image-size source partitioning, we prove separate necessary conditions for the existence of average-error and maximum-error codes. These necessary conditions are much stronger than the standard Fano's inequality, and can be weakened to render versions of Fano's inequality that apply to codes with non-vanishing error probabilities. To demonstrate the power of this new tool, we employ the stronger average-error version of Fano's inequality to prove the strong converse for the discrete memoryless wiretap channel with decaying leakage, which heretofore has been an open problem.

Connected through hyperedge HE-1 (induced by source paper "inducing information stability and applications thereof to obtaining information theoretic necessary conditions directly from operational requirements"):
  with members:
    - "wiretap channel capacity secrecy criteria strong converse and phase change"
    - "equating the achievable exponent region to the achievable entropy region by partitioning the source"

Connected through hyperedge HE-2 (induced by source paper "partial strong converse for the non degraded wiretap channel"):
  with members:
    - "new strong converse for asymmetric broadcast channels"
    - "exponent function for source coding with side information at the decoder at rates below the rate distortion function"
    - "strong converse exponent for state dependent channels with full state information at the sender and partial state information at the receiver"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"
    - "strong converse theorems for degraded broadcast channels with feedback"
    - "converses for secret key agreement and secure computing"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"

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)