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 "list decoding for oblivious arbitrarily varying macs constrained and gaussian"):
  Abstract: This paper provides upper and lower bounds on list sizes of list decoding for two-user oblivious arbitrarily varying multiple access channels (AVMACs). An oblivious AVMAC consists of two users who wish to transmit messages (without cooperation) to a remote receiver, a malicious jammer who only has access to the codebooks of both users (which are also known to every party), and a receiver who is required to decode the message pair sent by both users. The transmitters send codewords which encode messages subject to input constraints. The jammer, without knowing the transmitted codeword pair, injects adversarial noise subject to state constraints so as to actively corrupt the communication from both users to the receiver. It was left as an open question by Cai (2016) to nail down the smallest list sizes for constrained AVMACs. Our inner and outer bounds are based on a judicious notion of symmetrizability for AVMACs introduced by Cai (2016) with twists to incorporate input and state constraints. The analysis follows techniques by Csiszar and Narayan (1988). When no constraints are imposed, our bound collapse to prior results by Cai (2016) which characterized the list-decoding capacity region of unconstrained AVMACs. Techniques used in this paper can also be extended to the Gaussian case and we characterize the list-decoding capacity region for Gaussian AVMACs. The converse argument relies on a bounding technique recently used by Hosseinigoki and Kosut (2019).

Includes members:
  Member "the capacity region of the arbitrarily varying mac with and without constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quadratically constrained two way adversarial channels")
  Member "list decodable robust mean estimation and learning mixtures of spherical gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning for mixed linear regression")
  Member "a characterization of the capacity of online causal binary channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized list decoding")

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)