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: "learning hash functions using column generation"
  Abstract: Fast nearest neighbor searching is becoming an increasingly important tool in solving many large-scale problems. Recently a number of approaches to learning data-dependent hash functions have been developed. In this work, we propose a column generation based method for learning data-dependent hash functions on the basis of proximity comparison information. Given a set of triplets that encode the pairwise proximity comparison information, our method learns hash functions that preserve the relative comparison relationships in the data as well as possible within the large-margin learning framework. The learning procedure is implemented using column generation and hence is named CGHash. At each iteration of the column generation procedure, the best hash function is selected. Unlike most other hashing methods, our method generalizes to new data points naturally; and has a training objective which is convex, thus ensuring that the global optimum can be identified. Experiments demonstrate that the proposed method learns compact binary codes and that its retrieval performance compares favorably with state-of-the-art methods when tested on a few benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "deep multi index hashing for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "deep supervised discrete hashing"
    - "in defense of the triplet loss for person re identification"
    - "a revisit on deep hashings for large scale content based image retrieval"
    - "re ranking person re identification with k reciprocal encoding"
    - "stochastic generative hashing"

Connected through hyperedge HE-2 (induced by source paper "ssdh semi supervised deep hashing for large scale image retrieval"):
  with members:
    - "a survey on learning to hash"
    - "how should we evaluate supervised hashing"
    - "hashing on nonlinear manifolds"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "on the evaluation metric for hashing"):
  with members:
    - "deep supervised discrete hashing"
    - "stochastic generative hashing"
    - "hashnet deep learning to hash by continuation"
    - "how should we evaluate supervised hashing"
    - "optimizing affinity based binary hashing using auxiliary coordinates"
    - "self taught hashing for fast similarity search"

Connected through hyperedge HE-4 (induced by source paper "learning hash function through codewords"):
  with members:
    - "hash function learning via codewords"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "self taught hashing for fast similarity search"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-5 (induced by source paper "hashing with binary matrix pursuit"):
  with members:
    - "hashing with mutual information"
    - "mihash online hashing with mutual information"
    - "hashnet deep learning to hash by continuation"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "fast training of triplet based deep binary embedding networks"
    - "learning of generalized low rank models a greedy approach"
    - "deep supervised hashing with triplet labels"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"

Connected through hyperedge HE-6 (induced by source paper "deep saliency hashing"):
  with members:
    - "deep triplet quantization"
    - "deeply learned part aligned representations for person re identification"
    - "pose invariant embedding for deep person re identification"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"

Connected through hyperedge HE-7 (induced by source paper "error correction maximization for deep image hashing"):
  with members:
    - "hashing with mutual information"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-8 (induced by source paper "supervised deep hashing for hierarchical labeled data"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "return of the devil in the details delving deep into convolutional nets"

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.LG(Machine Learning)