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: "combinatorial limitations of average radius list decoding"
  Abstract: We study certain combinatorial aspects of list-decoding, motivated by the exponential gap between the known upper bound (of $O(1/\gamma)$) and lower bound (of $\Omega_p(\log (1/\gamma))$) for the list-size needed to decode up to radius $p$ with rate $\gamma$ away from capacity, i.e., $1-\h(p)-\gamma$ (here $p\in (0,1/2)$ and $\gamma > 0$). Our main result is the following: #R##N#We prove that in any binary code $C \subseteq \{0,1\}^n$ of rate $1-\h(p)-\gamma$, there must exist a set $\mathcal{L} \subset C$ of $\Omega_p(1/\sqrt{\gamma})$ codewords such that the average distance of the points in $\mathcal{L}$ from their centroid is at most $pn$. In other words, there must exist $\Omega_p(1/\sqrt{\gamma})$ codewords with low "average radius." The standard notion of list-decoding corresponds to working with the maximum distance of a collection of codewords from a center instead of average distance. The average-radius form is in itself quite natural and is implied by the classical Johnson bound. #R##N#The remaining results concern the standard notion of list-decoding, and help clarify the combinatorial landscape of list-decoding: #R##N#1. We give a short simple proof, over all fixed alphabets, of the above-mentioned $\Omega_p(\log (\gamma))$ lower bound. Earlier, this bound followed from a complicated, more general result of Blinovsky. #R##N#2. We show that one {\em cannot} improve the $\Omega_p(\log (1/\gamma))$ lower bound via techniques based on identifying the zero-rate regime for list decoding of constant-weight codes. #R##N#3. We show a "reverse connection" showing that constant-weight codes for list decoding imply general codes for list decoding with higher rate. #R##N#4. We give simple second moment based proofs of tight (up to constant factors) lower bounds on the list-size needed for list decoding random codes and random linear codes from errors as well as erasures.

Connected through hyperedge HE-1 (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"
    - "a characterization of the capacity of online causal binary channels"
    - "it ll probably work out improved list decoding through random operations"

Connected through hyperedge HE-2 (induced by source paper "it ll probably work out improved list decoding through random operations"):
  with members:
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"
    - "folded codes from function field towers and improved optimal rate list decoding"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "linear algebraic list decoding of folded reed solomon codes"
    - "subspace evasive sets"
    - "list decoding tensor products and interleaved codes"
    - "explicit codes achieving list decoding capacity error correction with optimal redundancy"

Connected through hyperedge HE-3 (induced by source paper "sparse approximation list decoding and uncertainty principles"):
  with members:
    - "on sparse representation in fourier and local bases"
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"
    - "the sparsity gap uncertainty principles proportional to dimension"

Connected through hyperedge HE-4 (induced by source paper "every list decodable code for high noise has abundant near optimal rate puncturings"):
  with members:
    - "on the list decodability of random linear codes with large error rates"
    - "folded codes from function field towers and improved optimal rate list decoding"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "subspace evasive sets"
    - "on the list decodability of random linear codes"
    - "explicit codes achieving list decoding capacity error correction with optimal redundancy"

Connected through hyperedge HE-5 (induced by source paper "on the list decodability of random linear codes with large error rates"):
  with members:
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "on the list decodability of random linear codes"
    - "noise tolerant learning the parity problem and the statistical query model"

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)