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 "construction and analysis of posterior matching in arbitrary dimensions via optimal transport"):
  Abstract: The posterior matching scheme, for feedback encoding of a message point lying on the unit interval over memoryless channels, maximizes mutual information for an arbitrary number of channel uses. However, it in general does not always achieve any positive rate; so far, elaborate analyses have been required to show that it achieves any positive rate below capacity. More recent efforts have introduced a random "dither" shared by the encoder and decoder to the problem formulation, to simplify analyses and guarantee that the randomized scheme achieves any rate below capacity. Motivated by applications (e.g. human-computer interfaces) where (a) common randomness shared by the encoder and decoder may not be feasible and (b) the message point lies in a higher dimensional space, we focus here on the original formulation without common randomness, and use optimal transport theory to generalize the scheme for a message point in a higher dimensional space. By defining a stricter, almost sure, notion of message decoding, we use classical probabilistic techniques (e.g. change of measure and martingale convergence) to establish succinct necessary and sufficient conditions on when the message point can be recovered from infinite observations: Birkhoff ergodicity of a random process sequentially generated by the encoder. We also show a surprising "all or nothing" result: the same ergodicity condition is necessary and sufficient to achieve any rate below capacity. We provide applications of this message point framework in human-computer interfaces and multi-antenna communications.

Includes members:
  Member "variable length codes for channels with memory and feedback error exponent lower bounds":
    also connected through other hyperedges:
      (none)
  Member "an efficient feedback coding scheme with low error probability for discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active learning and csi acquisition for mmwave initial alignment")
  Member "a simple proof for the optimality of randomized posterior matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active learning and csi acquisition for mmwave initial alignment")
  Member "extrinsic jensen shannon divergence applications to variable length coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low complexity algorithms for transmission of short blocks over the bsc with full feedback")
  Member "searching with measurement dependent noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active learning and csi acquisition for mmwave initial alignment")
  Member "collaborative 20 questions for target localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "resolution limits of noisy 20 questions estimation")
  Member "bit wise unequal error protection for variable length block codes with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the awgn red alert problem")
  Member "active sequential hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity")

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)