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 guesswork on swiftly tilting curves"
  Abstract: Given a collection of strings, each with an associated probability of occurrence, the guesswork of each of them is their position in an ordered list from most likely to least likely, breaking ties arbitrarily. Guesswork is central to several applications in information theory: Average guesswork provides a lower bound on the expected computational cost of a sequential decoder to decode successfully the intended message; the complementary cumulative distribution function of guesswork gives the error probability in list decoding; the logarithm of guesswork is the number of bits needed in optimal lossless one-to-one source coding; and guesswork is the number of trials required of an adversary to breach a password protected system in a brute-force attack. In this paper, we consider memoryless string-sources that generate strings consisting of i.i.d. characters drawn from a finite alphabet, and characterize their corresponding guesswork. Our main tool is the tilt operation. We show that the tilt operation on a memoryless string-source parametrizes an exponential family of memoryless string-sources, which we refer to as the tilted family. We provide an operational meaning to the tilted families by proving that two memoryless string-sources result in the same guesswork on all strings of all lengths if and only if their respective categorical distributions belong to the same tilted family. Establishing some general properties of the tilt operation, we generalize the notions of weakly typical set and asymptotic equipartition property to tilted weakly typical sets of different orders. We use this new definition to characterize the large deviations for all atypical strings and characterize the volume of weakly typical sets of different orders. We subsequently build on this characterization to prove large deviation bounds on guesswork and provide an accurate approximation of its PMF.

Connected through hyperedge HE-1 (induced by source paper "fair resource allocation in federated learning"):
  with members:
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "a reductions approach to fair classification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "mismatched guesswork"):
  with members:
    - "universal randomized guessing with application to asynchronous decentralized brute force attacks"
    - "why botnets work distributed brute force attacks need no synchronization"
    - "centralized vs decentralized multi agent guesswork"
    - "guesswork subject to a total entropy budget"
    - "multi user guesswork and brute force security"
    - "list decoding random coding exponents and expurgated exponents"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "erasure list random coding error exponents are not universally achievable"

Connected through hyperedge HE-3 (induced by source paper "guessing random additive noise decoding with symbol reliability information srgrand"):
  with members:
    - "capacity achieving guessing random additive noise decoding"
    - "on the origin of polar coding"
    - "the random coding bound is tight for the average linear code or lattice"
    - "guesswork large deviations and shannon entropy"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "guessing based on length functions"
    - "iterative algebraic soft decision list decoding of reed solomon codes"

Connected through hyperedge HE-4 (induced by source paper "guessing random additive noise decoding with soft detection symbol reliability information sgrand"):
  with members:
    - "capacity achieving guessing random additive noise decoding"
    - "on the origin of polar coding"
    - "guesswork large deviations and shannon entropy"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "guessing based on length functions"
    - "iterative algebraic soft decision list decoding of reed solomon codes"

Connected through hyperedge HE-5 (induced by source paper "universal compression with side information from a correlated source"):
  with members:
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "packet level network compression realization and scaling of the network wide benefits"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "results on the redundancy of universal compression for finite length sequences"

Connected through hyperedge HE-6 (induced by source paper "on the conditional smooth renyi entropy and its applications in guessing and source coding"):
  with members:
    - "non asymptotic fundamental limits of guessing subject to distortion"
    - "non asymptotic and asymptotic fundamental limits of guessing subject to distortion"
    - "multi user guesswork and brute force security"
    - "on the smooth renyi entropy and variable length source coding allowing errors"
    - "variable length compression allowing errors extended"
    - "variable length compression allowing errors"
    - "guesswork large deviations and shannon entropy"
    - "guessing revisited a large deviations approach"

Connected through hyperedge HE-7 (induced by source paper "why botnets work distributed brute force attacks need no synchronization"):
  with members:
    - "centralized vs decentralized multi agent guesswork"
    - "guesswork subject to a total entropy budget"
    - "guessing attacks on distributed storage systems"
    - "multi user guesswork and brute force security"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "guesswork large deviations and shannon entropy"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "guessing revisited a large deviations approach"

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)