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: "strong converse theorems for degraded broadcast channels with feedback"
  Abstract: We consider the discrete memoryless degraded broadcast channels with feedback. We prove that the error probability of decoding tends to one exponentially for rates outside the capacity region and derive an explicit lower bound of this exponent function. We shall demonstrate that the information spectrum approach is quite useful for investigating this problem.

Connected through hyperedge HE-1 (induced by source paper "strong converses are just edge removal properties"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "on the capacity advantage of a single bit"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "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 converse exponent for degraded broadcast channels at rates outside the capacity region"
    - "equivalence for networks with adversarial state"
    - "a case where interference does not affect the channel dispersion"

Connected through hyperedge HE-2 (induced by source paper "exponent function for stationary memoryless channels with input cost at rates above the capacity"):
  with members:
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"

Connected through hyperedge HE-3 (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"
    - "equal image size source partitioning creating strong fano s inequalities for multi terminal discrete memoryless channels"
    - "converses for secret key agreement and secure computing"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"

Connected through hyperedge HE-4 (induced by source paper "new strong converse for asymmetric broadcast channels"):
  with members:
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"
    - "error exponents for broadcast channels with degraded message sets"

Connected through hyperedge HE-5 (induced by source paper "exponent function for source coding with side information at the decoder at rates below the rate distortion function"):
  with members:
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"

Connected through hyperedge HE-6 (induced by source paper "strong converse exponent for state dependent channels with full state information at the sender and partial state information at the receiver"):
  with members:
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"

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)