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: "new strong converse for asymmetric broadcast channels"
  Abstract: Here the set X corresponds to a channel input and the sets Y and Z correspond to two channel outputs. Let X be a random variable taking values in X. We write an element of X as x = x1x2· · ·xn. Suppose that X has a probability distribution on X denoted by pXn = {pXn(x)}xn∈Xn . Similar notations are adopted for other random variables. Let Y n ∈ Y and Z ∈ Y be random variables obtained as the channel output by connecting X to the input of channel. We write a conditional distribution of (Y , Z) on given X as W = {W(y, zn|xn)}(xn,yn,zn)∈Xn×Yn×Zn .

Connected through hyperedge HE-1 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information"):
  with members:
    - "strong converse using change of measure arguments"
    - "exponential strong converse for content identification with lossy recovery"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "strong successive refinability and rate distortion complexity tradeoff"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "on cooperative multiple access channels with delayed csi at transmitters"

Connected through hyperedge HE-2 (induced by source paper "a new inequality related to proofs of strong converse theorems for source or channel networks"):
  with members:
    - "strong converse using change of measure arguments"
    - "exponent function for one helper source coding problem at rates outside the rate region"

Connected through hyperedge HE-3 (induced by source paper "exponential strong converse for content identification with lossy recovery"):
  with members:
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "second order region for gray wyner network"
    - "the dispersion of universal joint source channel coding for arbitrary sources and additive channels"
    - "non asymptotic converse bounds and refined asymptotics for two lossy source coding problems"
    - "wyner s common information under r enyi divergence measures"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "achievable moderate deviations asymptotics for streaming compression of correlated sources"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"

Connected through hyperedge HE-4 (induced by source paper "time division is optimal for covert communication over some broadcast channels"):
  with members:
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "optimal throughput for covert communication over a classical quantum channel"
    - "quantum secure covert communication on bosonic channels"
    - "reliable deniable communication hiding messages in noise"
    - "limits of reliable communication with low probability of detection on awgn channels"

Connected through hyperedge HE-5 (induced by source paper "partial strong converse for the non degraded wiretap channel"):
  with members:
    - "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"
    - "equal image size source partitioning creating strong fano s inequalities for multi terminal discrete memoryless channels"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "strong converse theorems for multimessage networks with tight cut set bound"):
  with members:
    - "achievable rates for gaussian degraded relay channels with non vanishing error probabilities"
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "on the reliability function of the discrete memoryless relay channel"
    - "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"
    - "wireless network information flow a deterministic approach"
    - "reliability in source coding with side information"
    - "capacity of a class of modulo sum relay 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)