human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generalized list decoding"):
  Abstract: This paper concerns itself with the question of list decoding for general adversarial channels, e.g., bit-flip ($\textsf{XOR}$) channels, erasure channels, $\textsf{AND}$ ($Z$-) channels, $\textsf{OR}$ channels, real adder channels, noisy typewriter channels, etc. We precisely characterize when exponential-sized (or positive rate) $(L-1)$-list decodable codes (where the list size $L$ is a universal constant) exist for such channels. Our criterion asserts that: #R##N#"For any given general adversarial channel, it is possible to construct positive rate $(L-1)$-list decodable codes if and only if the set of completely positive tensors of order-$L$ with admissible marginals is not entirely contained in the order-$L$ confusability set associated to the channel." #R##N#The sufficiency is shown via random code construction (combined with expurgation or time-sharing). The necessity is shown by #R##N#1. extracting equicoupled subcodes (generalization of equidistant code) from any large code sequence using hypergraph Ramsey's theorem, and #R##N#2. significantly extending the classic Plotkin bound in coding theory to list decoding for general channels using duality between the completely positive tensor cone and the copositive tensor cone. In the proof, we also obtain a new fact regarding asymmetry of joint distributions, which be may of independent interest. #R##N#Other results include #R##N#1. List decoding capacity with asymptotically large $L$ for general adversarial channels; #R##N#2. A tight list size bound for most constant composition codes (generalization of constant weight codes); #R##N#3. Rederivation and demystification of Blinovsky's [Bli86] characterization of the list decoding Plotkin points (threshold at which large codes are impossible); #R##N#4. Evaluation of general bounds ([WBBJ]) for unique decoding in the error correction code setting.

Includes members:
  Member "list decodable linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "list decodable subspace recovery")
  Member "list decodable learning via sum of squares":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "list decodable subspace recovery")
  Member "list decodable robust mean estimation and learning mixtures of spherical gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning for mixed linear regression")
  Member "list decodable zero rate codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on two fold packings of radius 1 balls in hamming graphs")
  Member "an improved bound on the fraction of correctable deletions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally decodable codes with randomized encoding")
  Member "upper bound on list decoding radius of binary codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on two fold packings of radius 1 balls in hamming graphs")
  Member "a characterization of the capacity of online causal binary channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "list decoding for oblivious arbitrarily varying macs constrained and gaussian")
  Member "it ll probably work out improved list decoding through random operations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "list decoding random euclidean codes and infinite constellations")

Question: Please tell me which class the source paper 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)