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: "causal erasure channels"
  Abstract: We consider the communication problem over binary causal adversarial erasure channels. Such a channel maps $n$ input bits to $n$ output symbols in $\{0,1,\wedge\}$, where $\wedge$ denotes erasure. The channel is causal if, for every $i$, the channel adversarially decides whether to erase the $i$th bit of its input based on inputs $1,...,i$, before it observes bits $i+1$ to $n$. Such a channel is $p$-bounded if it can erase at most a $p$ fraction of the input bits over the whole transmission duration. Causal channels provide a natural model for channels that obey basic physical restrictions but are otherwise unpredictable or highly variable. For a given erasure rate $p$, our goal is to understand the optimal rate (the capacity) at which a randomized (stochastic) encoder/decoder can transmit reliably across all causal $p$-bounded erasure channels. In this paper, we introduce the causal erasure model and provide new upper bounds and lower bounds on the achievable rate. Our bounds separate the achievable rate in the causal erasures setting from the rates achievable in two related models: random erasure channels (strictly weaker) and fully adversarial erasure channels (strictly stronger). Specifically, we show: #R##N#- A strict separation between random and causal erasures for all constant erasure rates $p\in(0,1)$. #R##N#- A strict separation between causal and fully adversarial erasures for $p\in(0,\phi)$ where $\phi \approx 0.348$. #R##N#- For $p\in[\phi,1/2)$, we show codes for causal erasures that have higher rate than the best known constructions for fully adversarial channels. #R##N#Our results contrast with existing results on correcting causal bit-flip errors (as opposed to erasures) [Dey et. al 2008, 2009], [Haviv-Langberg 2011]. For the separations we provide, the analogous separations for bit-flip models are either not known at all or much weaker.

Connected through hyperedge HE-1 (induced by source paper "generalizing multiple access wiretap and wiretap ii channel models achievable rates and cost of strong secrecy"):
  with members:
    - "a new wiretap channel model and its strong secrecy capacity"
    - "semantic security capacity for wiretap channels of type ii"
    - "achievability proof via output statistics of random binning"
    - "information theoretically secure regenerating codes for distributed storage"
    - "the gaussian many to one interference channel with confidential messages"
    - "the role of feedback in two way secure communications"
    - "discrete memoryless interference and broadcast channels with confidential messages secrecy rate regions"
    - "secure nested codes for type ii wiretap channels"

Connected through hyperedge HE-2 (induced by source paper "quadratically constrained channels with causal adversaries"):
  with members:
    - "the capacity of online causal q ary error erasure channels"
    - "a characterization of the capacity of online causal binary channels"
    - "on avcs with quadratic constraints"
    - "upper bounds on the capacity of binary channels with causal adversaries"

Connected through hyperedge HE-3 (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"
    - "a characterization of the capacity of online causal binary 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-4 (induced by source paper "the capacity of online causal q ary error erasure channels"):
  with members:
    - "on the capacity of memoryless adversary"
    - "a characterization of the capacity of online causal binary 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-5 (induced by source paper "the benefit of a 1 bit jump start and the necessity of stochastic encoding in jamming channels"):
  with members:
    - "a characterization of the capacity of online causal binary channels"
    - "upper bounds on the capacity of binary channels with causal adversaries"

Connected through hyperedge HE-6 (induced by source paper "on the capacity of memoryless adversary"):
  with members:
    - "upper bounds on the capacity of binary channels with causal adversaries"
    - "beating the gilbert varshamov bound for online channels"
    - "optimal rate code constructions for computationally simple channels"
    - "binary causal adversary channels"

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

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)