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: "markov determinantal point processes"
  Abstract: A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contain a diverse set of items selected from a base set Y. For example, we might use a DPP to display a set of news headlines that are relevant to a user's interests while covering a variety of topics. Suppose, however, that we are asked to sequentially select multiple diverse sets of items, for example, displaying new headlines day-by-day. We might want these sets to be diverse not just individually but also through time, offering headlines today that are unlike the ones shown yesterday. In this paper, we construct a Markov DPP (M-DPP) that models a sequence of random sets {Yt}. The proposed M-DPP defines a stationary process that maintains DPP margins. Crucially, the induced union process Zt = Yt u Yt-1 is also marginally DPP-distributed. Jointly, these properties imply that the sequence of random sets are encouraged to be diverse both at a given time step as well as across time steps. We describe an exact, efficient sampling procedure, and a method for incrementally learning a quality measure over items in the base set Y based on external preferences. We apply the M-DPP to the task of sequentially displaying diverse and relevant news articles to a user with topic preferences.

Connected through hyperedge HE-1 (induced by source paper "seeker real time interactive search"):
  with members:
    - "building a conversational agent overnight with dialogue self play"
    - "deep contextualized word representations"
    - "diversifying music recommendations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "web scale responsive visual search at bing"
    - "flowqa grasping flow in history for conversational machine comprehension"
    - "boltzmann exploration done right"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-2 (induced by source paper "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"):
  with members:
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "self critical sequence training for image captioning"
    - "sequence level training with recurrent neural networks"
    - "predicting important objects for egocentric video summarization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "probabilistic typology deep generative models of vowel inventories"):
  with members:
    - "learning determinantal point processes"

Connected through hyperedge HE-4 (induced by source paper "query focused extractive video summarization"):
  with members:
    - "predicting important objects for egocentric video summarization"
    - "videoset video summary evaluation through text"
    - "learning determinantal point processes"
    - "bayesian query focused summarization"

Connected through hyperedge HE-5 (induced by source paper "efficient sampling for k determinantal point processes"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "notes on using determinantal point processes for clustering with applications to text clustering"
    - "diversifying sparsity using variational determinantal point processes"
    - "determinantal clustering processes a nonparametric bayesian approach to kernel based semi supervised clustering"
    - "learning determinantal point processes"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-6 (induced by source paper "diversified hidden markov models for sequential labeling"):
  with members:
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "determinantal clustering processes a nonparametric bayesian approach to kernel based semi supervised clustering"

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)