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: "a characterization of the capacity of online causal binary channels"
  Abstract: In the binary online (or "causal") channel coding model, a sender wishes to communicate a message to a receiver by transmitting a codeword $\mathbf{x} =(x_1,\ldots,x_n) \in \{0,1\}^n$ bit by bit via a channel limited to at most $pn$ corruptions. The channel is "online" in the sense that at the $i$th step of communication the channel decides whether to corrupt the $i$th bit or not based on its view so far, i.e., its decision depends only on the transmitted bits $(x_1,\ldots,x_i)$. This is in contrast to the classical adversarial channel in which the error is chosen by a channel that has a full knowledge on the sent codeword $\mathbf{x}$. #R##N#In this work we study the capacity of binary online channels for two corruption models: the {\em bit-flip} model in which the channel may flip at most $pn$ of the bits of the transmitted codeword, and the {\em erasure} model in which the channel may erase at most $pn$ bits of the transmitted codeword. Specifically, for both error models we give a full characterization of the capacity as a function of $p$. #R##N#The online channel (in both the bit-flip and erasure case) has seen a number of recent studies which present both upper and lower bounds on its capacity. In this work, we present and analyze a coding scheme that improves on the previously suggested lower bounds and matches the previously suggested upper bounds thus implying a tight characterization.

Connected through hyperedge HE-1 (induced by source paper "list decoding for oblivious arbitrarily varying macs constrained and gaussian"):
  with members:
    - "the capacity region of the arbitrarily varying mac with and without constraints"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"

Connected through hyperedge HE-2 (induced by source paper "generalized list decoding"):
  with members:
    - "list decodable linear regression"
    - "list decodable learning via sum of squares"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "list decodable zero rate codes"
    - "an improved bound on the fraction of correctable deletions"
    - "upper bound on list decoding radius of binary codes"
    - "it ll probably work out improved list decoding through random operations"
    - "list decoding reed muller codes over small fields"

Connected through hyperedge HE-3 (induced by source paper "covert communication over adversarially jammed channels"):
  with members:
    - "first and second order asymptotics in covert communication"
    - "quadratically constrained myopic adversarial channels"
    - "second order asymptotics in covert communication"
    - "sufficiently myopic adversaries are blind"
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "quantum secure covert communication on bosonic channels"
    - "lpd communication when the warden does not know when"

Connected through hyperedge HE-4 (induced by source paper "quadratically constrained channels with causal adversaries"):
  with members:
    - "the capacity of online causal q ary error erasure channels"
    - "causal erasure channels"
    - "on avcs with quadratic constraints"
    - "upper bounds on the capacity of binary channels with causal adversaries"

Connected through hyperedge HE-5 (induced by source paper "sufficiently myopic adversaries are blind"):
  with members:
    - "a channel under simultaneous jamming and eavesdropping attack correlated random coding capacities under strong secrecy criteria"
    - "on the capacity of the binary adversarial wiretap channel"
    - "arbitrarily varying wiretap channels with type constrained states"
    - "the arbitrarily varying wiretap channel secret randomness stability and super activation"
    - "coding against a limited view adversary the effect of causality and feedback"
    - "semantic security capacity for wiretap channels of type ii"
    - "the arbitrarily varying wiretap channel deterministic and correlated random coding capacities under the strong secrecy criterion"
    - "secrecy results for compound wiretap channels"

Connected through hyperedge HE-6 (induced by source paper "coding against deletions in oblivious and online models"):
  with members:
    - "efficiently decodable insertion deletion codes for high noise and high rate regimes"
    - "an improved bound on the fraction of correctable deletions"
    - "causal erasure channels"
    - "upper bounds on the capacity of binary channels with causal adversaries"
    - "on the zero error capacity threshold for deletion channels"
    - "optimal rate code constructions for computationally simple channels"

Connected through hyperedge HE-7 (induced by source paper "the capacity of online causal q ary error erasure channels"):
  with members:
    - "on the capacity of memoryless adversary"
    - "causal erasure channels"
    - "upper bounds on the capacity of binary channels with causal adversaries"
    - "beating the gilbert varshamov bound for online channels"
    - "binary causal adversary channels"

Connected through hyperedge HE-8 (induced by source paper "the benefit of a 1 bit jump start and the necessity of stochastic encoding in jamming channels"):
  with members:
    - "causal erasure channels"
    - "upper bounds on the capacity of binary channels with causal adversaries"

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)