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: "rateless codes for avc models"
  Abstract: The arbitrarily varying channel (AVC) is a channel model whose state is selected maliciously by an adversary. Fixed-blocklength coding assumes a worst-case bound on the adversary's capabilities, which leads to pessimistic results. This paper defines a variable-length perspective on this problem, for which achievable rates are shown that depend on the realized actions of the adversary. Specifically, rateless codes are constructed which require a limited amount of common randomness. These codes are constructed for two kinds of AVC models. In the first the channel state cannot depend on the channel input, and in the second it can. As a byproduct, the randomized coding capacity of the AVC with state depending on the transmitted codeword is found and shown to be achievable with a small amount of common randomness. The results for this model are proved using a randomized strategy based on list decoding.

Connected through hyperedge HE-1 (induced by source paper "quadratically constrained myopic adversarial channels"):
  with members:
    - "list decoding random euclidean codes and infinite constellations"
    - "the arbitrarily varying gaussian relay channel with sender frequency division"
    - "covert communication over adversarially jammed channels"
    - "quadratically constrained channels with causal adversaries"
    - "sufficiently myopic adversaries are blind"
    - "a channel under simultaneous jamming and eavesdropping attack correlated random coding capacities under strong secrecy criteria"
    - "the benefit of a 1 bit jump start and the necessity of stochastic encoding in jamming channels"
    - "arbitrarily varying wiretap channels with type constrained states"

Connected through hyperedge HE-2 (induced by source paper "upper bounds on the capacity of binary channels with causal adversaries"):
  with members:
    - "beating the gilbert varshamov bound for online channels"
    - "binary causal adversary channels"
    - "explicit capacity achieving codes for worst case additive errors"
    - "codes against online adversaries"

Connected through hyperedge HE-3 (induced by source paper "zero rate feedback can achieve the empirical capacity"):
  with members:
    - "on the gaussian mac with imperfect feedback"
    - "feedback communication over individual channels"
    - "balancing forward and feedback error correction for erasure channels with unreliable feedback"
    - "feedback capacity of stationary gaussian channels"
    - "a counterexample to cover s 2p conjecture on gaussian feedback capacity"

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)