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: "on the reliability function of the discrete memoryless relay channel"
  Abstract: Bounds on the reliability function for the discrete memoryless relay channel are derived using the method of types. Two achievable error exponents are derived based on partial decode-forward and compress-forward, which are well-known superposition block-Markov coding schemes. The derivations require combinations of the techniques involved in the proofs of Csiszar-Korner-Marton’s packing lemma for the error exponent of channel coding and Marton’s type covering lemma for the error exponent of source coding with a fidelity criterion. The decode-forward error exponent is evaluated on Sato’s relay channel. From this example, it is noted that to obtain the fastest possible decay in the error probability for a fixed effective coding rate, one ought to optimize the number of blocks in the block-Markov coding scheme assuming the blocklength within each block is large. An upper bound on the reliability function is also derived using ideas from Haroutunian’s lower bound on the error probability for point-to-point channel coding with feedback.

Connected through hyperedge HE-1 (induced by source paper "strong converse theorems for multimessage networks with tight cut set bound"):
  with members:
    - "new strong converse for asymmetric broadcast channels"
    - "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 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"

Connected through hyperedge HE-2 (induced by source paper "achievable rates for gaussian degraded relay channels with non vanishing error probabilities"):
  with members:
    - "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "optimum power control at finite blocklength"
    - "simple channel coding bounds"

Connected through hyperedge HE-3 (induced by source paper "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"):
  with members:
    - "renyi divergence and kullback leibler divergence"
    - "wireless network information flow a deterministic approach"
    - "slepian wolf coding over cooperative relay networks"
    - "capacity of a class of modulo sum relay channels"

Connected through hyperedge HE-4 (induced by source paper "error and erasure exponents for the broadcast channel with degraded message sets"):
  with members:
    - "achievable error exponents for channel with side information erasure and list decoding"
    - "error exponents for broadcast channels with degraded message sets"
    - "a neyman pearson approach to universal erasure and list decoding"
    - "error exponents of erasure list decoding revisited via moments of distance enumerators"

Connected through hyperedge HE-5 (induced by source paper "multiuser random coding techniques for mismatched decoding"):
  with members:
    - "a counter example to the mismatched decoding converse for binary input discrete memoryless channels"
    - "expurgated random coding ensembles exponents refinements and connections"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "error exponent for multiple access channels lower bounds"

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)